the development of a swarm-based exploration …staff.iium.edu.my/amelia/mcs(g1426941).pdf · 2016....

THE DEVELOPMENT OF A SWARM-BASED

EXPLORATION ALGORITHM WITH THE EXPANDED

SQUARE PATTERN USING QUADCOPTER

BY

MUHAMMAD FUAD RIZA ZUHRI

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

2016

THE DEVELOPMENT OF A SWARM-BASED

EXPLORATION ALGORITHM WITH THE EXPANDED

SQUARE PATTERN USING QUADCOPTER

BY

MUHAMMAD FUAD RIZA ZUHRI

A thesis submitted in fulfilment of the requirement for the

degree of Master of Computer Science

Kulliyyah of Information and Communication Technology

International Islamic University Malaysia

SEPTEMBER 2016

http://www.google.com.my/url?url=http://www.iium.edu.my/educ&rct=j&frm=1&q=&esrc=s&sa=U&ei=KHqFVJaTIZKyuATNwoGoBw&ved=0CBMQFjAA&usg=AFQjCNH8CPBB4-yr6XSF1EeEZS5f3iT02w

ii

ABSTRACT

Exploration algorithm is one of the most important roles in searching mechanism. In

robotics field, exploration algorithm deals with the implementation of the robot to

enlarge the information over a particular environment. In other words, the

implementation of exploration algorithm into the robot is intended to survey the

situation or condition of a specific area. Based on that comprehension, exploration is

applicable to various field such as search and rescue, monitoring conservation,

scientific space exploration, etc. Although the field of exploration algorithm on

robotic has become a major research area and been studied since the 1950s, the

exploration problem has always been an interesting topic for investigation. A variety

of techniques has been developed, even the biological systems have also become an

inspiration to be reckoned. In this thesis, we propose a swarm-based exploration

algorithm with the expanded square pattern using the quadcopter to explore an

unknown area. In this algorithm, the expanded square pattern is conducted by a series

of the distance around a fixed reference point. We simulate the swarm-based

exploration algorithm with the expanded square pattern in the VREP simulator. The

existing exploration algorithms namely, the frontier baseline and the cellular automata

are also simulated to be compared with the proposed algorithm. All algorithms are

simulated with the same setup. In order to analyse and evaluate the performance of all

algorithms, the data of the simulation are documented. Some comparisons are

conducted such as the performance of all algorithms, the performance of a group of

the quadcopter, the covered spaces and the cooperation among groups. According to

the simulation results, the swarm-based exploration algorithm with the expanded

square pattern can explore better and faster compared to the frontier baseline and the

cellular automata as the number of robots increased. This is supported by the

statistical analysis that is conducted at the end of this research.

iii

ملخص البحثC

في آلية البحث. خوارزمية التنقيب تنقيبالفي مجال الروبوتات، خوارزمية تلعب دورا مهما

بعبارة أخرى، فإن خوارزمية التنقيب في. تاتكبير المعلوم تزود الروبوت في بيئة معينة بقابلية

ينطبق على فإن التنقيبعلى ذلك الفهم، بناء . منطقة معينة شروطدراسة حالة أو ب تعنى الروبوت

الفضاء العلمي، وما إلى واستكشاف ،المراقبة والصيانة مختلف المجاالت مثل البحث واإلنقاذ،

خوارزمية التنقيب الروبوتية أصبحت منطقة بحثية رئيسية وتمت على الرغم من أن .ذلك

لتنقيبشكلة افإن م، 1950عام منذ تهادراس موضوع ما تكون دائما . حتى اآلن للبحث ا مثير ا

ت، وحتى النظم البيولوجية أصبحتم تطويرها مجموعة متنوعة من التقنيات مصدر إلهام ال أيضا

مربع توسعي مع نمط يةفي هذه األطروحة، اقترحنا خوارزمية التنقيب السرببه. يستهان

في هذه الخوارزمية، .الستكشاف منطقة مجهولة طوافة رباعية المراوح )كوادكوبتر(استخدام ب

في هذا .حول نقطة مرجعية ثابتة اتمربع من خالل سلسلة من المسافالتوسعي ال نمطيتم تأدية ال

ية مع النمط التوسعي المربع باستخدام برنامج محاكاة خوارزمية التنقيب السربنقوم بالبحث،

تمت خوارزميات التنقيب الحالية التي تم تحديدها . VREPلمحاكاة ا لغرض محاكاتها أيضا

ات. نفس اإلعداد باستخدامتمت محاكات جميع الخوارزميات .مع الخوارزمية المقترحة تهامقارن

ريتأج. ثم توثيق البيانات من المحاكاةتم تحليل وتقييم أداء جميع الخوارزميات، لغرض

المساحات و كوادكوبتر،المثل أداء جميع الخوارزميات، وأداء مجموعة من بينها مقارنات

على التحليل اإلحصائي الذي ا داعتمايمكن أن نخلص .المغطاة، والتعاون بين المجموعات

و جري في نهاية هذا البحثأ نمط المع يسربالخوارزمية التنقيب أن لنتيجة المحاكاة، وفقا

.أن تؤدي بشكل أفضل وأسرع كلما زاد عدد الروبوتات مكنيالتوسعي المربع

iv

APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion, it conforms

to acceptable standards of scholarly presentation and is fully adequate, in scope and

quality, as a thesis for the degree of Master of Computer Science

…………………………………..

Amelia Ritahani Ismail

Supervisor

I certify that I have read this study and that in my opinion it conforms to acceptable

standards of scholarly presentation and is fully adequate, in scope and quality, as a

thesis for the degree of Master of Computer Science

…………………………………..

Rizal Mohd. Nor

Internal Examiner

…………………………………..

Mohammad Faidzul Nasrudin

External Examiner

This thesis was submitted to the Department of Computer Science and is accepted as a

fulfilment of the requirement for the degree of Master of Computer Science

…………………………………..

Normi Sham Awang Abu Bakar

Head, Department of Computer

Science

This thesis was submitted to the Kulliyyah of Information and Communication

Technology and is accepted as a fulfilment of the requirement for the degree of Master

of Computer Science

…………………………………..

Abdul Wahab Abdul Rahman

Dean, Kulliyyah of Information

and Communication Technology

v

DECLARATION

I hereby declare that this thesis is the result of my own investigations, except where

otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Muhammad Fuad Riza Zuhri

Signature ........................................................... Date .........................................

vi

COPYRIGE

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF

FAIR USE OF UNPUBLISHED RESEARCH

THE DEVELOPMENT OF A SWARM-BASED EXPLORATION

ALGORITHM WITH THE EXPANDED SQUARE PATTERN

USING QUADCOPTER

I declare that the copyright holders of this thesis is Muhammad Fuad Riza Zuhri

Copyright © 2016 Muhammad Fuad Riza Zuhri. All rights reserved.

No part of this unpublished research may be reproduced, stored in a retrieval

system, or transmitted, in any form or by any means, electronic, mechanical,

photocopying, recording or otherwise without prior written permission of the

copyright holder except as provided below

1. Any material contained in or derived from this unpublished research

may be used by others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print

or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved system

and supply copies of this unpublished research if requested by other

universities and research libraries.

By signing this form, I acknowledged that I have read and understand the IIUM

Intellectual Property Right and Commercialization policy.

Affirmed by Muhammad Fuad Riza Zuhri

……..…………………….. ………………………..

Signature Date

vii

ACKNOWLEDGEMENTS

First and foremost, Alhamdulillah, all praises and thanks to Allah SWT, the Almighty,

for the strengths, the patience and His showers of blessings throughout my research

work to complete my thesis successfully, after all the challenges and difficulties.

I wish to express my appreciation to my supervisor, Dr. Amelia Ritahani

Ismail. I would not the person I am now without her guidance. Thank you for

introducing me to this research world. I could not have imagined having a better

advisor and mentor for my Master study. My appreciation to my fellow AI Group

members, who supported each other to ensure we would accomplish this goal.

Finally, it is my utmost pleasure to dedicate this work to my dear parents and

my family, for supporting me spiritually throughout writing this thesis and for making

me believe in myself and keep going. Thank you for your support and patience.

viii

TABLE OF CONTENTS

Abstract .................................................................................................................... ii

Abstract in Arabic .................................................................................................... iii

Approval Page .......................................................................................................... iv

Declaration ............................................................................................................... v

Copyright Page ......................................................................................................... vi

Acknowledgements .................................................................................................. vii

List of Tables ........................................................................................................... xi

List of Figures .......................................................................................................... xii

List of Algorithms xiv

CHAPTER 1: INTRODUCTION ........................................................................ 1

1.1 Research Background ............................................................................. 1

1.2 Statement of the Problem........................................................................ 4

1.3 Research Hypotheses .............................................................................. 5

1.4 Research Objectives................................................................................ 5

1.5 Research Questions ................................................................................. 5

1.6 Contribution ............................................................................................ 6

1.7 Thesis Structure ...................................................................................... 6

CHAPTER 2: LITERATURE REVIEW ............................................................ 7

2.1 Introduction............................................................................................. 7

2.2 Path Planning .......................................................................................... 8

2.3 Navigation System .................................................................................. 10

2.4 Exploration Algorithm ............................................................................ 13

CHAPTER 3: RESEARCH METHODOLOGY ............................................... 23

3.1 Research Methodology ........................................................................... 23

3.1.1 Studying the Literature Survey ..................................................... 23

3.1.2 Designing the Quadcopter in Simulation ...................................... 23

3.1.3 Simulating Swarm-based Exploration Algorithm to the

Quadcopter .................................................................................... 25

3.1.4 Analysing the Algorithm Performance of the Quadcopter ........... 27

3.1.5 Comparing the Proposed Algorithm with the Other

Algorithms .................................................................................... 27

3.1.6 Documenting the Result of Simulation, Evaluation,

Comparison and Analysis ............................................................. 28

CHAPTER 4: THE SWARM-BASED EXPLORATION ALGORITHM

WITH THE EXPANDED SQUARE PATTERN ................... 29

4.1 The Swarm-based Exploration Algorithm with the Expanded Square

Pattern ..................................................................................................... 29

CHAPTER 5: EXPERIMENTAL SETUP ......................................................... 36

5.1 Simulation Platform ............................................................................... 36

5.2 Experimental Protocol ............................................................................ 37

ix

CHAPTER 6: SIMULATION ............................................................................. 42

6.1 Experiment I: The Swarm-based Exploration Algorithm with

Expanded Square Pattern ........................................................................ 42

6.1.1 Experiment I: Two Quadcopters ................................................... 42

6.1.2 Experiment I: Four Quadcopters ................................................... 43

6.1.3 Experiment I: Eight Quadcopters .................................................. 43

6.2 Experiment II: The Swarm-based Exploration Algorithm based on

Frontier Based Approach ........................................................................ 47

6.2.1 Experiment II: Two Quadcopters .................................................. 47

6.2.2 Experiment II: Four Quadcopters ................................................. 48

6.2.3 Experiment II: Eight Quadcopters ................................................ 50

6.3 Experiment III: The Swarm-based Exploration Algorithm with

Cellular Automata .................................................................................. 53

6.3.1 The Virtual Mapping in the Cellular Automata ............................ 53

6.3.2 Experiment III: Two Quadcopters ................................................ 54

6.3.3 Experiment III: Four Quadcopters ................................................ 55

6.3.4 Experiment III: Eight Quadcopters ............................................... 57

CHAPTER 7: RESULT AND DISCUSSION ..................................................... 60

7.1 The Performance of Exploration Algorithm ........................................... 60

7.1.1 Experiment I: The Swarm-based Exploration Algorithm with

Expanded Square Pattern .............................................................. 60

7.1.1.1 Result and Evaluation ....................................................... 61

7.1.1.2 Analysis: The Vargha-Delaney A Test ............................. 62

7.1.2 Experiment II: The Swarm-based Exploration Algorithm

based on Frontier Based Approach ............................................... 63



7.1.3 Experiment III: The Swarm-based Exploration Algorithm with

Cellular Automata......................................................................... 66



7.2 The Performance of Groups of Quadcopters .......................................... 68

7.2.1 The Group of Two Quadcopters ................................................... 68


7.2.2 The Group of Four Quadcopters ................................................... 70


7.2.3 The Group of Eight Quadcopters .................................................. 72


7.3 The Covered Space of All Algorithms ................................................... 74

7.3.1 The Comparison of Different Number of Square Pattern ............. 75

7.3.2 The Comparison of Covered Spaces ............................................. 76

7.4 The Cooperation among Quadcopters .................................................... 78

7.4.1 The Group of Two Quadcopters ................................................... 78

7.4.2 The Group of Four Quadcopters ................................................... 79

7.4.3 The Group of Eight Quadcopters .................................................. 80

7.4.4 The Cooperation of the Quadcopters in terms of

Communication ............................................................................ 81

x

CHAPTER 8: CONCLUSION ............................................................................. 85

REFERENCES ....................................................................................................... 88

xi

LIST OF TABLES

Table 6.1 The Duration of the Expanded Square Pattern Algorithm for

Different Number of Quadcopters 47

Table 6.2 The Duration of the Frontier Baseline Algorithm for Different

Number of Quadcopters 51

Table 6.3 The Duration of the Cellular Automata Algorithm for Different

Number of Quadcopters 58

Table 7.1 The Magnitude of the Effect Size Indicated by A Test Score:

Square Pattern 62


Frontier Baseline 65


Cellular Automata 68


Two Quadcopters 70


Four Quadcopters 72


Eight Quadcopters 73

Table 7.7 The Number of Communication and the Covered Space

Comparison of the Expanded Square Pattern 82

Table 7.8 The Number of Communication and the Covered Space

Comparison of the Cellular Automata 82

Table 7.9 The Measurement of the Effectiveness Communication 83

xii

LIST OF FIGURES

Figure 3.1 The Flowchart of the Research Methodology 24

Figure 4.1 General Concept of Exploration Activity 30

Figure 4.2 Cardinal Directions (Cardinal Points) 31

Figure 4.3 The Expanded Square Pattern 32

Figure 5.1 The Design of the Quadcopter in the VREP Simulator 38

Figure 5.2 The Design of the Environment in the VREP Simulator 39

Figure 5.3 The Staring Point of the Expanded Square Pattern Quadcopters 39

Figure 5.4 The Starting Point of the Frontier Baseline Quadcopters 40

Figure 5.5 The Starting Point of the Cellular Automata Quadcopters 40

Figure 6.1 Two Quadcopter Simulation on Experiment I 44

Figure 6.2 Four Quadcopter Simulation on Experiment I 45

Figure 6.3 Eight Quadcopter Simulation on Experiment I 46

Figure 6.4 Two Quadcopter Simulation on Experiment II 49

Figure 6.5 Four Quadcopter Simulation on Experiment II 50

Figure 6.6 Eight Quadcopter Simulation on Experiment II 52

Figure 6.7 The Virtual Map in the Cellular Automata 53

Figure 6.8 The Result of the Cellular Automata in the Virtual Map

Viewpoint 54

Figure 6.9 Two Quadcopter Simulation on Experiment III 56

Figure 6.10 Four Quadcopter Simulation on Experiment III 57

Figure 6.11 Eight Quadcopter Simulation on Experiment III 59

Figure 7.1 The Comparison of the Different Number of Quadcopter’s

Performance in the Exploration Algorithm with the Expanded

Square Pattern 61

xiii


Performance in the Exploration Algorithm based on the

Frontier Based Approach 64


Performance in the Exploration Algorithm based on the

Cellular Automata 67

Figure 7.4 The Comparison of the Algorithm’s Performance for the

Group of Two Quadcopters 69


Group of Four Quadcopters 71


Group of Eight Quadcopters 73

Figure 7.7 The Comparison of Each Scenario in terms of the Number of

Square Pattern Performed for Each Quadcopter 75

Figure 7.8 The Space Comparison for Two Quadcopters 77

Figure 7.9 The Space Comparison for Four Quadcopters 77

Figure 7.10 The Space Comparison for Eight Quadcopters 77

Figure 7.11 The Uncovered Space (Red Colour) 78

Figure 7.12 The Comparison of Duration among Group of Two

Quadcopters 79

Figure 7.13 The Comparison of Duration among Group of Four

Quadcopters 80

Figure 7.14 The Comparison of Duration among Group of Eight

Quadcopters 81

xiv

LIST OF ALGORITHMS

Algorithm 1 Exploration 30

Algorithm 2 Expanded Square Pattern 32

Algorithm 3 Covered Area 33

Algorithm 4 Next Location Planning 34

1

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Exploration is one of the most important utilities as a searching activity to obtain much

information in an unknown environment. It is the basic role for a searching activity

because exploration serves as the main contribution in collecting information. The best

result which can be achieved only if exploration can be completed. Without

completing the exploration, the result of a searching activity cannot be affirmed. In

ensuring a good exploration, two properties must be realized; completeness and

effectiveness in terms of space and time respectively. Completeness requires the

explorer to cover most of the area while effectiveness emphasizes the explorer’s

efficiency to complete the exploration in minimum time.

Nowadays, the exploration activity has been studied to make major

contributions in various fields. The researchers give more attention to this field in

order to support Search and Rescue (SAR) team on the job. It is because, sometimes,

the effort and result of the exploration are not good enough. Therefore, many

researches such as Alvissalim et al. (2012), Di Felice et al. (n.d.), Apvrille et al. (2014)

and Ma’sum et al. (2013) have been conducted to support the search and rescue team.

Those projects are trying to make some contribution that can help the SAR team do

their jobs. Various aspects of technology have been developed to reduce the risk in the

search and rescue mission that can endanger people or animals such as horses and

dogs usually involved in searching. The application of robots can retrieve information

much more easily and safely compared to people or animal.

2

However, the use of an autonomous robot has recently become a priority to

support the search mechanism. The unmanned aerial vehicle (UAV) is a kind of

vehicle that operate without a human pilot aboard. As an example, Cantelli et al.

(2013), Tanner (2007), Grocholsky et al. (2006) and Phan and Liu (2008) have proved

UAV can be used to cover large areas in the search for targets, moving rapidly or

seeing through such obstacles as buildings or fences that cannot be done by the

unmanned ground vehicle (UGV). Apvrille et al. (2014) and Ma’sum et al. (2013) give

an example of exploration as something that develops the autonomous drones that can

fly autonomously to cover certain areas and identify groups of people. Most of the

implementation applies the searching mechanism for finding people and the

exploration mechanism has a main role to support search and rescue.

In exploration, there are two elements that must be completed; path planning to

determine the area for the next exploration and the navigation system to manage the

movement of the quadcopter. These two elements should be completed appropriately

because of their main roles to discover an unknown area.

The concept of the swarm robot and the quadcopter as a kind of flying robot

has become two interesting ideas combined and developed in the exploration activity.

Some advantages of the multi-robot approaches described by Arkin (1998) that are a

group of robots can perform more efficiently and actuate at different places

simultaneously, a group of robots has a wider range of sensing than a single robot and

can accomplish certain goals which are impossible for a single robot.

A quadcopter is also a helicopter that is lifted and propelled by four rotors. The

use of quadcopter has advantages as mentioned by Bouabdallah et al. (2004), Patel et

al. (2012), Xu and Ozguner (2006), Zul Azfar and Hazry (2011), Gupte et al. (2012),

Lee et al. (2009) and Bou-Ammar et al. (2010): this kind of UAV offers the payload

3

augmentation, quadcopter as a simple unmanned vehicle to manufacture and control,

the navigation of quadcopter allows simple take-off/landing if it is compared to a fixed

wing aircraft. It is a hybrid of a fixed wing aircraft because of its manoeuvrability that

becomes its inherent dynamic nature and a kind of unmanned aerial vehicle, its flight

time depending on the fuel/battery life.

After looking at those implementations, it can be seen that a multi-flying robot

contributes an impact to a search mission. Thus, the use of robots, usually called

swarm robots, can be considered as a good option to develop an application to support

a search mission. In this context, the swarm-robot system is often suggested to have

obvious advantages over the single-robot system: faster, robust, fault-tolerant and

compensation of sensors uncertainty. To minimize the time to complete the

exploration task in the context of a swarm-robot exploration, efficient exploration

techniques should consider strategies to distribute the robots in the environment to

reduce an overlap among the explored areas of each robot. This is the global

coordination issue defined as allocating appropriately exploration goals for the

individual robots so that they simultaneously explore the different zones of the area.

The flying robot can also be selected as a type of robot which is more reliable in this

research, a type of flying robot, the quadcopter which is selected based on its

advantages.

In this research, we propose the development of a swarm-based exploration

algorithm with the expanded square pattern for an outdoor environment using the

quadcopter in large areas. The expanded square pattern adopted from the National

Search and Rescue Manual, Australia by Authority (2014). The basic idea of the

expanded square pattern is to start the pattern from a fixed central point and to expand

4

outwards in concentric squares. Some quadcopters will be designed in mass in order to

achieve a great exploration performance.

1.2 STATEMENT OF THE PROBLEM

Exploration is one of the important elements in a search mechanism. The search

mechanism needs the exploration technique to discover unknown areas. However,

some problems may occur e.g., the path to be discovered cannot be estimated by the

robot (Al Redwan Newaz et al., 2013). Hence, robots cannot estimate whether they

have covered the whole area or not. Therefore, the exploration should be conducted in

such a way where it can cover a large area. It becomes an important factor to be

considered so the completeness of an exploration can be predicted.

Moreover, other problems may also occur when the area to be covered is too

large. If the robots explore in the same direction, they will need more time to explore

another direction and avoid colliding with each other (Doniec et al., 2009; Zelenka and

Kasanicky, 2014). In this kind of environment, a method for the deployment of robots

must be considered because it will affect the effectiveness of the exploration. Each

robot should know its own area to which it belongs.

Furthermore, in a search mission conducted by a team, coordination among

members is very important for the division of tasks. It means that if each member does

not know the status of the other members regarding their positions and conditions,

they may end up exploring the same area that had already been covered. In other

words, the repetition can be a waste of time. On the other hand, space limitation in the

environment can force multiple robots to move together and they can also interfere

with each other (Julia et al., 2010; Zelenka and Kasanicky, 2014). The more robots

5

used to accomplish the goal, the more time needed on detours in order to avoid a

collision. Therefore, coordination among all robots must be managed properly.

1.3 RESEARCH HYPOTHESES

The following are some hypotheses relating to this research:

H1 The swarm-based exploration algorithm can be successfully applied for

exploring a large area.

H2 The proposed swarm-based exploration algorithm can be implemented to a

swarm of quadcopters for their coordinated movement based on different

directions.

1.4 RESEARCH OBJECTIVES

This study embarks on the following objectives:

1- To study the existing exploration algorithms.

2- To develop a swarm-based exploration algorithm for the quadcopter.

3- To simulate the exploration algorithm in a large area.

4- To compare the performance of the swarm-based exploration algorithms

with the other swarm-based algorithms.

1.5 RESEARCH QUESTIONS

In this research, the focus is to answer the following important questions:

1. What are the exploration algorithms that have been developed?

2. What kind of exploration algorithm can be implemented to a swarm of the

quadcopters?

3. How effective is the proposed swarm-based exploration algorithm for

covering a large area compared to the other swarm-based exploration

algorithms?

6

1.6 CONTRIBUTION

The major contribution of this research is the development of a swarm-based

exploration algorithm with the expanded square patterns. This algorithm will be

simulated using the quadcopters. This algorithm is intended for outdoor environments

in a large area. This algorithm is developed based on the coordinated movement of

swarm robots.

1.7 THESIS STRUCTURE

This thesis is divided into 8 chapters. In chapter 2, we provide a critical review of the

related works and literature on the exploration algorithm, path planning and navigation

system. This chapter will clarify the common problem of the existing exploration

algorithm and identify the proper navigation system and path planning to be utilized.

In chapter 3, we provide the research methodology that describes our attempts to

create the swarm-based exploration algorithm with the expanded square pattern. In

chapter 4, we provide an explanation of the swarm-based exploration algorithm with

the expanded square pattern. In chapter 5, we describe the experiment that will be

organized for the proposed algorithm and the other two existing exploration

algorithms. In chapter 6, the simulation of exploration algorithm that is implemented

to a different number of robots in different scenarios is illustrated. In chapter 7, we

provide our result and discussion about the performance of the swarm-based

exploration algorithm compared to other swarm-based exploration algorithms. Finally,

Chapter 8 will be dedicated to the conclusion of the development of the swarm-based

exploration algorithm with expanded square patterns.

7

CHAPTER TWO

LITERATURE REVIEW

2.1 INTRODUCTION

Environment exploration is a domain in robotic research that studies the robot

behavior in discovering an area. In real life, robot explorations are used in the

dangerous environmental investigation and the search and rescue field which do not

allow the participation of human beings. Some research has been conducted and

expanded into the other subfields such as coordinated deployment and distribution,

searching mechanism, monitoring system and other.

The exploration algorithm consists of two major elements: mapping and path

planning as mentioned by Stachniss (2009). These two elements cannot be applied

independently. Mapping is an element that is needed by the robot to gather

information of the environment. It is used to answer the question “What does the

environment look like?” Some researchers use camera vision sensors to gather this

information while others use the sonar sensor or laser findings. Since this research

does not use any kind of sensor and camera, the role of mapping the environment will

be replaced by the navigation system. Hence, the robots in this research use the

navigation system to gather information about the environment. Path planning answers

the question “How can I reach a target location?” In this aspect, it needs the

information that is collected by mapping. These two elements can be combined to do

the exploration.

In this chapter, three main aspects of exploration algorithms will be explained

clearly: path planning in Section 2.2, the navigation system in Section 2.3 and Section

2.4 will explain the existing problems of the exploration algorithm.

8

2.2 PATH PLANNING

In Tisdale et al. (2009), path planning approach was developed to deal with the

myopic which can be a problem for the UAV in certain situations. This approach is to

choose a planning horizon to ensure that every plan has a value above some threshold

and it is called the increasing horizon planner strategy. The good thing about this

technique is that this system can be categorized as a unique for it allows the searching

and localization step in the same framework so it can reduce the complexity of its

process. However, this technique could not be applied in detecting more than one

moving target.

Khuswendi et al. (2011) explore the composition of the path planning

algorithm and claim it as the most appropriate algorithm for UAV path planning. The

algorithms proposed by Khuswendi et al. (2011) are based on the potential field

method and A* algorithm because these two methods have similarities. Because the

2D A* algorithm and A* 3D hierarchical methods had problems about being time

consuming and short path problem respectively, eventually, the A* 3D receding

horizon method is applied by dividing the environment into 10×10×20. The advantage

of this algorithm is that it optimizes the constraint that occurs in other methods in

terms of safety, time and energy cost. However, this algorithm can be applied if the

obstacle has been determined.

The biological system can also inspire the path planning technique. Galvez et

al. (2014) adopt the genetic algorithm for path planning. The process starts with

initializing the start point and goal point in 3D coordinates. The genetic algorithm will

evaluate its fitness. The obstacle is then identified in the chromosome. The next step is

the selection process where the first half of high fitness chromosome is used to

generate offspring and as a result, the fittest chromosome is the lowest value of

9

distance traveled. Here, it alters each gene with a small probability and applies a

random search to make sure all points have been examined as described by Mitchell

(1998).

In addition, according to Faigl et al. (2010), a simple straight line segment path

between two goals increases the error in the segment (longitudinal) direction because

of imprecise odometry. In order to solve this problem, the robot can traverse to any

point close to the goal point. So, the proposed algorithm by Faigl et al. (2010) would

use the SOM adaptation schema for the TSP modified using equation (4): Ai+1 =

RTiMiRiAiR

TiMi

TRi + RTiSiRi mentioned by Faigl et al. (2010) in his paper that created

the ring of points. This technique can be applied in a situation where there is more

than one goal point but the localization error and not recognizing the goal and position

can be a serious problem.

A two-level path planning algorithm is proposed by Ok et al. (2013) and called

Voronoi Uncertainty Fields (VUF). The higher level planner modifies the generalized

Voronoi diagrams introduced by Lee and Drysdale (1981) for a collision-free path

exists. The lower level planner considers the observed obstacle in the environment

using the potential field method introduced by Khatib (1986). The top-level planner

can be used as the global planner to create and update a list of Voronoi nodes that

make the shortest path. The bottom-level can be used as the local planner that uses the

list as a local way-point. However, Ok et al. (2013) combine these two methods with

the SLAM system. The advantages of this method are that it is designed for an

uncertain environment, it moves robots out of local minima and it provides a forest-

like environment. However, it does not deal with map uncertainties in a deterministic

and complete way.

10

In summary, after looking at those path planners and considering the

advantages and disadvantages of each algorithm, Ok et al., (2013) gives some ideas to

this research i.e., to divide path planning into two levels: the higher level deals with

the global planner and the lower level deals with the local planner. Therefore, in the

development of the path planning algorithm, the concept of the local and global path

planning will be adopted by adjusting them with the swarm-based exploration

algorithm. The path taken should be recorded or memorized by the quadcopter so it

will not take the same path that can be time-wasting for exploration.

2.3 NAVIGATION SYSTEM

Navigation is a field of study that focuses on the process of monitoring and controlling

the movement of a craft or vehicle from one place to another. The field of navigation

includes four general categories: land navigation, marine navigation, aeronautic

navigation, and space navigation. The quadcopter navigation system will be

categorized as aeronautic navigation. The following are some work that has been done.

Krajnik et al. (2012) present a simple visual navigation system for an

autonomous quadcopter extended from a ground robot navigation system in Krajnıket

al. (2010). The method is based on the “record and replay” technique. So, the record

technique means that the UAV will traverse along the (poly-line shape) path and

during this movement, it will track or “record” the salient features in an image from an

on-board camera. The quadcopter could then perform autonomous flight towards the

first recorded path segment by applying the replay technique. Here, the quadcopter

compares the mapped landmarks to the features in its field of view. The use of the

simple histogram voting scheme that makes methods swift and robust is the

advantages besides it does not require radio beacons and artificial landmarks.

11

However, the disadvantage is that it operates only along paths that have been travelled

before, only for the straight line and has the limitation in length.

Although this algorithm does not need to use a landmark, the method from

Krajnik et al. (2012) is similar to what has been done by Apvrille et al. (2013) and

Selby et al. (2011). All depend on the condition or situation of the environment. It

means that the environment has to provide them with some information. It can be seen

from the Apvrille et al. (2013) project.

For the indoor environment, Apvrille et al. (2013) introduce the autonomous

navigation using landmark and 3D perception with a bottom and a front camera and

on-board sensors. In this project, there are three recognitions applied which follow a

coloured line on the floor, to identify landmark and to capture the environment in 3D

without a 3D sensor. Two techniques are described here, proposed by Ranft et al.

(2013). The use of the landmark and the prediction of the moving object are two

disadvantage of this system as well as it is only for the indoor environment.

Moreover, in a project, visual control for the quadcopter navigation system is

developed by Selby et al. (2011). An independent and on-board vision-based control

system to autonomously identify and track a moving target. The motion captures

feedback and is replaced by an estimated state measurement from an Extended

Kalman Filter (EKF) from Bachrach et al. (2009) and Bachrach et al. (2011).

However, Selby et al. (2011) also provide the GPS system. The GPS plays an

important role in this technique when the system is in an autonomous mode. However,

it has not been proven in a real ocean. The controller also depends on the visual

navigation and there is a limitation of intended target.

To prevent from being dependent on environmental information, a project by

Engel et al. (2012) creates a system where a ground-based laptop can navigate

12

autonomously in an unknown and GPS-denied environment. So, this approach utilizes

three components running on a laptop and connected via wireless LAN to the

quadcopter. The first is the monocular SLAM based on PTAM as described by Klein

and Murray (2007). The second is EKF to fuse all available data. One advantage is it

does not need an artificial landmark and knowledge about the environment.

Unfortunately, it uses off-board processing, in other words, it depends on the laptop.

Thus, the limitation of distance also becomes a problem.

Krokowicz et al. (2010) also introduce another solution to prevent from

problems of Krajnik et al. (2012), Apvrille et al. (2013) and Selby et al. (2011). For

the indoor navigation system, Krokowicz et al. (2010) use schematic environment

maps and sensor information from ultrasonic sensors. In this algorithm, the robot has

to identify the characteristic points and based on these points, the robot position can be

identified. The schematic map and ultrasonic sensor give benefit to this system

because it is easy and cheap. But it is only for the indoor environment and the absence

of camera and sensor reading is its flaw.

For this matter, Rengarajan and Anitha (2013) has a good solution in using the

GPS for the navigation system. They also develop an algorithm for an autonomous

way-point navigation using the GPS and Atmega-328P. In this project, the GPS is

used as an input sensor to get the latitude and longitude of the quadcopter’s current

position. There are two good things about this system: the use of the GPS and that it

flows along predefined tracks. However, the system depends on its base station

(laptop) for the whole mechanism so the limitation of distance is also a short-coming.

It can be summarized that for the navigation system, the use of the GPS

becomes a main factor for the effective movement of the quadcopter as shown by

Selby et al. (2011) and Rengarajan and Anitha (2013). It is because the quadcopter can

13

find the exact point coordinated in term of (x, y) for latitude and longitude. Some

research has been conducted relying on the camera to guide the movement of the

quadcopter. However, in this research, the use of the camera is not a proper way

because it is used to detect objects and in a certain condition, it cannot give an accurate

guidance to the quadcopter.

2.4 EXPLORATION ALGORITHM

Exploration is the act of searching for the purpose of the discovery of information or

resources. Exploration of unknown environments has become one of the interesting

problems in robotics. This work requires a robot to explore and at the same time to

learn about the covered area so that the area can be identified and recognized. A multi-

robot system has made the contribution to this research field. In this section, we want

to look for various methods that have been done and to get some ideas of the existing

exploration methods. Most divide the exploration method into several stages that will

be executed depending on the robot’s situation and some have developed exploration

algorithm inspired by the biological system.

Many researchers have published the exploration algorithm that falls into the

algorithm of the frontier-based exploration such as Yamauchi (1997), Yamauchi et al.

(1998), Makarenko et al. (2002) and Gonzalez-Banos and Latombe (2002). They

create a strategy based on the idea that robot(s) attempt to obtain as much new

information as possible from the environment explored by going to the boundary

between the area that had been explored and unexplored (Pravitra et al., 2011). The

random selection technique has always become an option in this problem study.

However, when we implement it in real life, the random selection may be an

inefficient technique.

14

Yamauchi (1997) says the basic idea of a frontier based approach is:

”To gain the most new information about the world, move to the

boundary between open space and uncharted territory.”

By constantly moving to new frontiers, the robot can gain more information

about the new territory and extend the map. In this algorithm, when the robot can

navigate to a certain position, it means that that area is considered accessible for

exploration. Many researchers have worked such as Fraundorfer et al., (2012), Freda

and Oriolo, (2005) and Simmons et al., (2000) that relate to this algorithm and some

has improvised it.

Franchi et al. (2009) and Cesare et al. (2015) have shown experiments about

the exploration algorithm. In Franchi et al. (2009), a decentralized strategy for

cooperative robot exploration has been developed. A simple and decentralized

cooperation mechanism becomes the basic idea of this method. Each robot moves

towards areas that appear to be unexplored by the rest of the team on the basis of the

available information. However, Cesare et al. (2015) offer a new method inspired by

Franchi et al. (2009) that would be explained later.

A similar idea is also described by De Hoog et al. (2009) where the robots

would cooperate to store information of the covered areas. Role-based autonomous

exploration algorithm is proposed by De Hoog et al. (2009). In this method, there will

be two mobile robots and each is assigned one of two roles at the beginning of

exploration and do not change. The two roles are explorer which means to explore the

farthest reaches of the environment and relay which helps to connect explorers to the

command centre. Thus, firstly, a mobile robot will explore an area as far as it can and

then periodically returns to a previous rendezvous point to pass its knowledge to a

relay mobile robot. After that, the relay mobile robot communicates the findings of the

15

explorer mobile robot to the command centre. If it finds an information of the

environment while relaying, this information will be added to the findings of the

explorer mobile robot. However, De Hoog et al. (2009)’s method has a limitation of

communication and it is limited by a static team hierarchy. Maintaining them could

lead to a long travel to the rendezvous point.

In 2009, works about the exploration method under communication constraint.

Doniec et al. (2009) make a proposal that is claimed as the original way to formalize

and solve the issue that relies on the distributed constraint satisfaction problems

(disCSP) which are an extension of the classical constraint satisfaction problem (CSP)

by Kumar (1992). There are five states implemented on this algorithm. The first is to

update maps and connectivity tables for each robot. The second is to construct the

disCSP based on the connectivity table and the robot’s current position. The next state

is to order the value of each domain taking the distance to the frontier into account.

The fourth is to solve the disCSP to obtain the next direction and finally, to operate the

movement of each robot during a fixed time period. All these five states will be

repeated until there are no more unexplored areas. Each robot must exchange its local

map with each other to build a global view of the environment in order to detect those

five states. It guarantees the connectivity among all members, it is easier for deadlock

detection and it decreases the duration when adding the number of robots. But, when

the robot is added, the team spends more time avoiding each other than exploring. The

size of the disCSP also increases requiring more messages. The algorithm is

asynchronous so the delay occurs when exchanging messages.

So far, it can be seen that those algorithms have problems, such as leading to

long travel to tryst point or getting stuck at a certain point which can result in

inefficient exploration and the distribution of the robot that can waste time. Therefore,

16

Julia et al. (2010) and Cepeda et al. (2012) have tried to solve this kind of problem.

These two works will be explained below.

Julia et al. (2010) introduce a hybrid/deliberative approach to the multi-robot

for exploration problem. One exploration problem is the negative effect of local

minima that has been presented by Lau and NSW (2003) and Julia et al. (2008) where

it wastes time in the process to escape from that point. Basically, this approach relies

on the concept of an expected safe zone that inspired the concept of safe zone

implemented by Gonzalez-Banos and Latombe (2002) and Franchi et al. (2007) and

the gateway cell. There are two layers for the movements of the robot: the reactive

layer (state: go to frontier, avoid obstacle or go to gateway) for the expected safe zone

and the deliberative layer used to switch or combine several states. Moreover, the

hybrid/deliberative approach has advantages such as the avoidance of local minima,

the reactive process runs in a delimited period of time caused by the expected safe

zone concept and all these show the robustness of the algorithm. However, usually, if

more robots are implemented and the result should be better. However, in this case, it

increases the error because too many robots have to cover a small area travel in a

shorter path more time. Therefore it is needed in the changed zone state for

coordination.

Cepeda et al. (2012) have introduced a simple exploration algorithm that

combines a behaviour-based navigation with an efficient data structure to store path

taken. The proposed algorithm implements four different behaviours and a resultant

emergent behavior. The first is to avoid obstacles which considers three conditions:

the first condition is the possible corner to avoid getting stuck, the second is to keep

distance from obstacle and the last is to avoid team-mates. The second behaviour is to

avoid past introduced by Balch (1993) which is used to gather the newest location by

17

using a hash table to store the path taken before. The third behaviour is to locate an

open area, used for locating the largest open area. This behavior represents the

wandering factor of the exploration technique. The four behaviour is dispersion as

introduced by Mataric (1995) that implement the coordination mechanism to spread

the robots and avoiding team-mates. The last behaviour is a resultant emergent

behaviour that uses a Finite State Automata (FSA) to decide which state should be

activated. This constitutes an important part of this exploration algorithm. The good

things are it can cover a large open space and not get stuck nor spend unnecessary time

because of the use of the hash table. Unfortunately, it depends on the communication

with the other robots so there is a limitation in the distance and the quality of the map

is also not good.

Furthermore, for the multi-robot team, the communication range may be a case

so the coordination that was under limited communication has to be brought into

account. However, rendezvous strategy has been implemented to solve the limited

communication problem. It can be seen from Ko et al. (2003), Roy and Dudek (2001)

and Zhou and Roumeliotis (2006). Mosteo et al. (2008) also address the same

problem. Yuan et al. (2010) introduce a cooperative approach for multi-robot

exploration that adopts the frontier-based algorithm introduced firstly by Yamauchi

(1997). The optimal frontier will be selected by evaluating information gain and

navigation cost and consider the communication range. Thus, the robot team calculates

the set of frontier cells and select the candidates from this set. The evaluating index

includes information gain and navigation cost as mentioned before. When the robot

explores the unknown area, the number of frontier cells will also increase.

Unfortunately, because of that, the computation complexity would be over. Therefore,

the frontier cells can be partitioned into different groups and each group evaluated by

18

using the subtractive clustering algorithm introduced by (Chiu, 1994) to estimate the

initial centre and the number of groups. The result of clustering will be as some

candidate cells and these will be integrated by each robot. So, the selected destination

of the robot will be the region enveloped by the frontier and finally, it is known.

Eventually, the exploration of the unknown environment can be converted into a

problem of multi-stage trajectory planning in the known environment which consists

of two sub-processes including avoiding to disperse the robot team and tracking to

synchronous rendezvous for the multi-robot. The efficient and distributed exploration

give some benefit so it can minimize the navigation cost. However, since it calculates

local destinations without enough information, it needs more exploration step. It does

not consider the motion and measurement uncertainty and has limited

communications.

After observing the exploration for the mobile robot, the following paragraph

will lead us to see the exploration especially for the flying robot such as MAV or the

quadcopter. Some researchers adopt the idea from the mobile robot’s exploration

algorithm and others create their own algorithm. However, so far, many researchers

still implement the idea introduced by Yamauchi (1997) as their basic exploration

movement combined with other techniques.

An exploration algorithm has been presented by Shen et al. (2012a) and Shen

et al. (2012b). It is called the SDE-based exploration algorithm that enables MAV to

explore in 3D indoor environments. So, the exploration algorithm is used to explore

the free space. Here, the free space is represented by a set of virtual particles that is

resampled based on its density to identify the representation of the environment. It

means that the dense of the particle is for the known space while the sparse is for the

unknown space. There are three states described by Shen et al. (2012a) and Shen et al.

19

(2012b): initializing and re-sampling, simulating particle expansion and extracting

frontiers as the Stochastic Differential Equation-based Exploration algorithm (SDEE).

After initializing which is to generate and emit the particle at the known free space

location, the next step is re-sampling by detecting the regions of the greatest expansion

of particle. After the free space is identified, the frontiers are selected and the robot

navigates to that location and does full exploration. Unfortunately, some flaws can be

seen from this project. The first is that it is only for the indoor environment and a

single robot. Then, there is no information about the number of particles required in

expanding particle for the certain environment. Based on the experimental setup, if it

is compared to the frontier-based, it needs more path and time and the percentage of

covered area is less than the frontier based.

Additionally, Sang et al. (2013) describe an exploration and an obstacle

avoidance method design to be implemented on indoor MAV running in cluttered and

confined indoor environment. Since Sang et al. (2013)’s work aims to three problems,

it uses the frontier based strategy from (Yamauchi, 1997) for the basic exploration idea

of the MAV. For getting waypoint, they use the safe corridor method and the SLAM

navigation system is implemented for estimating position. They use the safe corridor

and the waypoint tree where the child node is selected corresponding to the longest

frontier as the next waypoint. If no child node is feasible, the MAV returns to the last

node and removes the current node. It can be seen that there is a chance where the

robot returns to the explored area and if it is implemented in multi-robot, there is a

chance where two or more robots will cover the same area. In other words, the

efficiency of this algorithm is not as expected. There is also a possibility where the

MAV does not explore the area completely.

20

Al Redwan Newaz et al. (2013) proposes a heuristic algorithm for exploration

priority. In this algorithm, the UAV is used to test their algorithm in V-REP simulator.

They say that their algorithm envisions a new direction for online path planning based

on the fact that the obstacle does not always hinder from reaching a goal position,

rather, sometime, it helps to find a goal position easily. In other words, they try to use

the obstacle to reach the goal position and invokes the obstacle to be the guidance. In

this algorithm, Al Redwan Newaz et al. (2013) introduces four steps: grid making to

find the nearest next set position, cost estimation to restrict the movement options of

UAV, obstacle search to avoid from any obstacle and moving to minimum cost point

to navigate its position to the new location. Moreover, Al Redwan Newaz et al. (2013)

claims their algorithm for having less search although their path is not the shortest.

Besides that, this algorithm does not ensure the UAV can cover the whole area. The

use of a heuristic algorithm in path planning also does not always guarantee to find the

goal position even if it can set the next movement and the indoor environment become

the characteristic of this algorithm’s implementation.

It can be seen that problems appear with the exploration by a single robot. The

exploration cannot be achieved for all kinds of environment. The range of exploration

by a single robot is not as large as multiple robots and there is no assurance that the

robot can cover the area as expected. However, limitations can be solved by using

multiple robots when they can cooperate. Some works have been done to overcome

these problems that will be explained in the following paragraph.

Another work about swarm robot that implements the exploration algorithm is

introduced by Cesare et al. (2015). They utilize four states: explore, meet, sacrifice and

relay. This method is compared to a baseline frontier-based exploration approach by

Franchi et al. (2009) where the baseline frontier-based approach is the same as the

21

state explore. The comparison shows that the improvement of using the states of meet,

sacrifice and relay can explore a greater percentage of the map (5% to 18%). This

method maximizes the efficiency of exploring with unreliable communication and

limited energy battery that can be predicted as well. However, it is just for a small

scale environment, not for a large environment and there is a possibility that the robot

cannot complete together. In other words, the completeness of an exploration cannot

be affirmed for all cases. Furthermore, some constraint of Cesare et al. (2015)’s work

cannot be considered such as this algorithm is only for the indoor environment and this

work is intended only for certain conditions which are unreliable communication and

limited energy battery.

Zelenka and Kasanicky (2014) is inspired to develop the exploration algorithm

based on cellular automata for the swarm robot. They investigate this algorithm in the

real outdoor environment using two quadcopters. In this approach, every robot has to

have its own map which is a cellular grid. Every robot creates its own map based on its

sensing and they will share information to gain the information of the environment.

Every change that is made is also updated. Coordination among the robots is done by

using the evaporated mark and robot behaviours. Once a single robot has visited an

area, it will put a mark on this area with virtual pheromones. For the exploration part,

the robot will divide the area into regular square cells and it can perform one step or

stay in position in every iteration movement. When a robot visits a grid cell, it gives a

mark such as virtual pheromones onto its map and direction and sends it to the other

robots. By doing that, it can share information. However, after a robot visits that area,

the other robots can visit the same area again since the first robot has moved to the

other grid cells and shared its own virtual pheromones. In other words, there is a

chance where two robots or more can discover the same area even if the time they visit

22

is different. It can be seen in Figure 3 in Zelenka and Kasanicky (2014). The

distribution of the swarm robot is also not managed. It means that the swarm robot can

move in the same direction at the same time.

After observing those exploration algorithms, the final problem leads us to

Zelenka and Kasanicky (2014)’s problem which is the possibility of robots to explore

the same area which can be a waste of time and the efficiency of an exploration in

relation to the number of robots is not proven statistically. The distribution of the robot

is also not evaluated to support the performance of the exploration. It is because the

distribution or deployment of robots can give impact to the efficiency and

effectiveness of the exploration activity.

It can be summarized that the use of multi robots in exploration can give better

results. In other words, a large environment can be covered by multiple robots with

less duration in finishing it. Moreover, the cooperation among robots in terms of their

deployment becomes an effective approach to increase the efficiency and effectiveness

of the exploration.

23

CHAPTER THREE

RESEARCH METHODOLOGY

This part of the thesis describes the way in which the work on simulating the searching

algorithm has been carried out. This chapter is related to the readings and findings

from Chapter 2. In Section 3.1, the research methodology is explained. The research

methodology is summarized in the flowchart in Figure 3.1.

3.1 INTRODUCTION

3.1.1 Studying the Literature Survey

In the first step, the literature survey is conducted to identify the basic idea of the

exploration algorithm. Those publications are analysed to study about the path

planning algorithm, the navigation system and the exploration algorithm. The

advantages and disadvantages of each research are identified to get a clear and deep

understanding of problems of the exploration algorithm that are going to be solved.

3.1.2 Designing the Quadcopter in Simulation

In the second step, the component to build a quadcopter is identified and determined

such as motor, body, camera, some sensors and navigation module. And then, start

constructing the body of quadcopter by designing it in the simulation. Since this

research only creates the quadcopter in simulation, some simulators have already

provided the quadcopter as a complete unit. However, some modules must be added to

support the performance of the quadcopter in completing the task such as GPS for the

navigation system and the wireless module for communication among units.

24

Figure 3.1 The Flowchart of the Research Methodology

GPS The Global Positioning System (GPS) is a space-based satellite navigation

system that provides location and time information in all weather conditions,

anywhere on or near the earth where there is an unobstructed line of sight to four or

more GPS satellites. The GPS system concept is based on time. The satellites carry

very stable atomic clocks that are synchronized to each other and to ground clocks.

GPS satellites continuously transmit their current time and position. A GPS receiver

monitors multiple satellites and solves equations to determine the exact position of the

receiver and its deviation from true time. The receiver’s earth-centered solution

25

location is usually converted to latitude, longitude and height relative to an ellipsoidal

earth model.

Wireless Communication V-REP allows simulating wireless communications in a

very flexible way: data can be emitted into a specific direction, and over a specific

distance. Emitted data can then be received if the receiver is located within the

specified emission area. Refer to the corresponding functions in the regular API for

more details. Wireless emission/reception activities can be visualized by enabling the

Visualize wireless emissions and Visualize wireless receptions items in the

environment dialogue.

After the quadcopter has been built, the simple navigation system is applied to

move from one point to another as simple as it is. If the quadcopter can move properly,

it means that the quadcopter can be used for this research and the other quadcopters

are also built by following the design of the first quadcopter. After the quadcopters are

ready, the communication among quadcopters are connected so later, those

quadcopters can communicate to each other in completing their tasks.

3.1.3 Simulating the Swarm-based Exploration Algorithm to the Quadcopter

After studying and analysing algorithms related to the exploration algorithm as

mentioned in Section 3.1.1, the best technique is adopted for the path planning and

navigation system. For example, the use of GPS can be an alternative as the

quadcopter can move from and to a specific point coordinate as used by (Selby et al.,

2011; Rengarajan and Anitha, 2013). The use of the GPS can be categorized as a

proper implementation of the navigation system compared to the other methods

mentioned in Chapter 2 Section 2.3. Furthermore, for the exploration algorithm, a new

simple algorithm will be designed and developed based on the national search and

26

rescue manual technique described in Authority (2014). There are adjustments which

must be done for this combination to get a complete exploration algorithm. It means

that there must be uncorrelated systems among those techniques and the adjustment

must be applied to fuse those techniques.

According to Authority (2014) mentioned in National Search and Rescue

Manual, Australia:

“In an expanding square search, the searching activity begins at a

position that is reported or most likely location. It will expand outwards

in concentric squares. It is an appropriate pattern to be implemented but

it requires accurate navigation to do this pattern. Usually, the first leg of

this pattern will be directed into the wind. It is applied to minimize and

prevent the navigational error. As a kind of search pattern in search and

rescue manual procedure, the square search pattern is always used when

the location of target is recognized to be in a small area relatively. It

means that the location of target is no more then 15−20 NM where 1

NM = 1.852 km, from the start point of searching.”

The first two legs are held to a distance that is equal to the spacing between the

track legs. After the same two legs are done, it will be increased by another track

spacing. The direction can be to the right or left. It depends on the position of the

observer’s point of view. After one searching is completed, the direction of the

searching should be changed by 45◦. However, the position of a final track should be

the same as the initial search track at the start point. The number of search legs can

vary, for instance, it may be 5 and it will be increased by an increment of 4, 9, 13, 17

and etc.

27

3.1.4 Analysing the Algorithm Performance of the Quadcopter

After implementing the new algorithm, analyses are conducted:

1. The movement of the quadcopter. It should be reviewed to see whether

the quadcopter moves properly from one point to another. It shows that if

there is an error in the movement, the exploration activity cannot be

completed as expected.

2. The cooperation among those quadcopters. It should be reviewed to see

whether those quadcopters can communicate and cooperate through that

communication network line. The network communication takes an

important role because it establishes the communication among those

quadcopters.

3. The exploration activity of each quadcopter. It must be analysed to see

whether those quadcopters have covered a determined area as expected.

The developed exploration algorithm must be applied properly to achieve

the main task of a quadcopter team.

4. The completion task. Finally, the performance must be analysed whether

those quadcopters can finish the whole task from start point until the goal

with the expected performance and result.

3.1.5 Comparing the Proposed Algorithm with the Other Algorithms

If the quadcopters have successfully completed their tasks based on the previous

experiment, the comparison is conducted to contrast with another method. The swarm-

based exploration algorithm implemented with the expanded square pattern will be

compared to another exploration algorithm introduced by Yamauchi (1997) to prove

that this swarm-based exploration algorithm has better results because of the expanded

28

square pattern compared to the random selection. It will also be compared to another

swarm-based exploration algorithm described by Zelenka and Kasanicky (2014) to

prove the deployment direction of this swarm-based approach shows improvements.

Some factors related to the performance of the quadcopter as a team is identified such

as time taken, path taken, search pattern and the expansion of the quadcopter’s covered

area during the exploration activity.

3.1.6 Documenting the Results of Simulation, Evaluation, Comparison and

Analysis

After previous sections (Section 3.1.1 until 3.1.5) have been done, the results of the

testing, the analysing and the comparison are documented. The chart line is created to

see the result of the comparisons specifically. The performance and achievement of the

new method are discussed in detail.

29

CHAPTER FOUR

THE SWARM-BASED EXPLORATION ALGORITHM WITH THE

EXPANDED SQUARE PATTERN

In this chapter, we will discuss the swarm-based exploration algorithm with the

expanded square pattern. In exploration, there are three algorithms that are used to

complete the task. The first is Algorithm 1 that describes the main idea of the

exploration activity. Inside Algorithm 1, there are three algorithms that will be

executed: Algorithm 2 for the expanded square pattern, Algorithm 4 for determining

the next location and Algorithm 3 for calculating the covered area.

4.1 THE SWARM-BASED EXPLORATION ALGORITHM WITH THE

EXPANDED SQUARE PATTERN

In this section, the main concept of the swarm-based exploration is introduced. Firstly,

the swarm robotic is a field of study that is concerned with controlling and

coordinating multiple robots and is defined by Dorigo et al. (2014) as:

“the study of how to design groups of robots that operate without

relying on any external infrastructure or on any form of centralized

control and in a robot swarm, the collective behavior of the robots

results from local interactions between the robots and between the

robots and the environment in which they act.”

Therefore, in this research, some quadcopters that can communicate with each

other to perform exploration activity will be used. The concept of this exploration

activity is shown in Figure 4.1.

30

Figure 4.1 General Concept of Exploration Activity

The main exploration algorithm is presented in Algorithm 1. The exploration

activity begins with initializing the point coordinate to start an exploration activity.

This point is derived from the user’s information. The point coordinate should consist

of two points: latitude and longitude that can be identified by the GPS as a guide to

these quadcopters. These points then are transferred to the quadcopters and calculated

by each of them.

Algorithm 1 Exploration

Require: finding location coordinate

initializing (x,y)currentlocation

initializing length of area

initializing width of area

if (x,y)currentlocation == true then

Ddirection ← (north|| east||south||west)

turning to Ddirection

endif

31

for i := (x,y)currentlocation → (x,y)centersquare do

moving forward

updating i

endfor

repeat

determining (x,y)targetsquare

turning right

executing Algorithm 2


checking status

if (x,y)coveredarea == false then


else

STOP

endif

until maximum of expansion radius

They will determine the direction of the exploration; one will go to north,

south, east or west respectively. It is decided based on the four cardinal directions

(cardinal points) as shown in Figure 4.2. After determining the direction, each

quadcopter turns to the respective directions. Afterward, every quadcopter will go to a

point to perform the expanded square pattern that is viewed in Algorithm 2. On

arriving there, each quadcopter will perform the expanded square pattern adopted from

the National Search and Rescue Manual, Australia by Authority (2014) as shown in

Figure 4.3.

Figure 4.2 Cardinal Directions (Cardinal Points)

32

Algorithm 2 Expanded Square Pattern

Require: finding location coordinate

while (x,y)currentlocation ≠ (x,y)targetsquare do

moving to (x,y)targetsquare

turning 90○ to left

updating (x,y)currentlocation

(x,y)targetsquare ← (x,y)targetsquare + 0.5 meters

endwhile

Figure 4.3 The Expanded Square Pattern

When they go across the maximum local radius point, they will stop and generate

communication lines to each other. They will communicate to determine the next area

to be explored. Prior to that, they will calculate the covered area of the expanded

square pattern. It is done by applying Algorithm 3. As shown in algorithm 3, at the

beginning, the robot must initialize its current location coordinate. Then, it does the

nested loop to store x and y coordinate to the array size[x][y]. The (x)diagonal and

(y)diagonal in algorithm 3 is derived from the equation 4.4. In the equation 4.4, the point

coordinate of the target square is subtracted by the coordinate of the expanded square

pattern centre and subtracted again by 0.5. After that, this result is used to subtracted

the coordinate of centre square to obtain the (x)diagonal and (y)diagonal.

33

Algorithm 3 Covered Area

if (x)currentlocation == true ˄ (y)currentlocation == true then

for i := (x)diagonal → (x)currentlocation do

for j := (y)diagonal → (y)currentlocation do

size[x][y] = (i,j)

endfor

endfor

endif

After they store the point coordinate of the covered area, they will determine

the next location described in Algorithm 4. The algorithm 4 covers the path planning

algorithm. In determining the next location, each quadcopter will send its current

location coordinate and covered area to the others. On receiving the coordinate point,

they will calculate it with their own coordinate point to find the middle point between

these two points. In this case, the calculation is controlled by the following rules:

1) the north quadcopter will calculate its coordinate with the east

quadcopter’s coordinate;

2) the east quadcopter will calculate its coordinate with the south


3) the south quadcopter will calculate its coordinate with the west


4) the west quadcopter will calculate its coordinate with the north

quadcopter’s coordinate.

To obtain the middle point, the equation 4.1 is implemented in Algorithm 4.

After receiving the partner location, the robot subtracted its point coordinate with its

partner point coordinate and divide it by two and plus the partner coordinate again. In

the next step, they will go to the middle point and turn right. After that, they will check

whether the next centre point of the expanded square pattern has been covered or not.

34

If it is no, the robot can continue to perform the next expanded square pattern.

However, if it is yes, they need to determine the location of the next expanded square

pattern by using the equation 4.3. In the equation 4.3, the value of range explore is

obtained from the equation 4.2. The value of length of area is divided by two and its

result is divided by four. After obtaining the value of range explore, in the equation

4.3, this value is multiplied by 2 and added by the current location of the robot. Then,

the robot can do the exploration again by using the same expanded square pattern.

These processes are repeated until they reach the maximum global radius point.

Algorithm 4 Next Location Planning

obtaining (x,y)currentlocation; (x,y)centersquare; (x,y)targetsquare

sending (x,y)currentlocation; (x,y)coveredarea

receiving (x,y)partnerlocation; (x,y)partnercoveredarea

(x,y)middlepoint ← equation 4.1

while (x,y)currentlocation ≠ (x,y)middlepoint do

moving to (x,y)middlepoint


endwhile

if (x,y)currentlocation == (x,y)middlepoint then

turning right

if (x,y)centersquare == (x,y)coveredarea then

(x,y)centersquare ← (x,y)targetsquare + 1.5

(x,y)targetsquare ← equation 4.2


moving forward


endfor

elseif (x,y)centersquare ≠ (x,y)coveredarea then


moving forward


endfor

endif

endif

35

Equations

(𝑥, 𝑦)𝑚𝑖𝑑𝑑𝑙𝑒𝑝𝑜𝑖𝑛𝑡

= (

(𝑥, 𝑦) 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛

− (𝑥, 𝑦)𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛

2) + (𝑥, 𝑦)𝑝𝑎𝑟𝑡𝑛𝑒𝑟

𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛

, 𝑤ℎ𝑒𝑟𝑒 ∀𝑥 ∈ [0, ∞) (4.1)

𝑟𝑎𝑛𝑔𝑒 𝑒𝑥𝑝𝑙𝑜𝑟𝑒 =

𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑎𝑟𝑒𝑎2⁄

4 (4.2)

(𝑥, 𝑦)𝑡𝑎𝑟𝑔𝑒𝑡

𝑠𝑞𝑢𝑎𝑟𝑒 = (𝑟𝑎𝑛𝑔𝑒 𝑒𝑥𝑝𝑙𝑜𝑟𝑒 ∗ 2) + (𝑥, 𝑦)𝑐𝑢𝑟𝑟𝑒𝑛𝑡

𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 (4.3)

(𝑥, 𝑦)𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙 = (𝑥, 𝑦)𝑐𝑒𝑛𝑡𝑒𝑟𝑠𝑞𝑢𝑎𝑟𝑒 − (((𝑥, 𝑦)𝑡𝑎𝑟𝑔𝑒𝑡𝑠𝑞𝑢𝑎𝑟𝑒 − (𝑥, 𝑦)𝑐𝑒𝑛𝑡𝑒𝑟𝑠𝑞𝑢𝑎𝑟𝑒) − 0.5) (4.4)

36

CHAPTER FIVE

EXPERIMENTAL SETUP

In this section, a quadcopter has been designed using V-REP (Virtual Robot

Experimentation Platform) by E. Rohmer (2013) that has become a model standard in

robotics research facilitating other researchers to conduct their works. The simulation

platform is discussed in Section 5.1. Here, the advantage of the V-REP is described.

Moreover, the experiment protocol is presented in Section 5.2. In this section, the

information of the experiment is explained.

5.1 SIMULATION PLATFORM

A robotic simulator is used to create embedded applications for the robot

without depending physically on the actual machine. The simulator used in the

experiment is V-REP. V-REP is the Swiss army knife among robots simulators. The

robot simulator V-REP, integrated development environment, is based on a distributed

control architecture: each object/model can be individually controlled via an

embedded script, a plugin, an ROS node, a remote API client, or a custom solution.

This makes V-REP very versatile and ideal for multi-robot applications. Controllers

can be written in C/C++, Python, Java, Lua, Matlab, Octave or Urbi. V-REP is used

for fast algorithm development, factory automation simulation, fast prototyping,

verification, robotics-related education, remote monitoring, safety double-checking,

etc.

In addition, V-REP is highly customizable simulator: every aspect of a

simulation can be customized. Moreover, the simulator itself can be customized and

tailored so as to behave exactly as desired. This is allowed through an elaborate

37

Application Programming Interface (API). Six different programming or coding

approaches are supported, each having particular advantages over the others but all six

are compatible.

Nogueira and Lucas (n.d.) compared V-REP and Gazebo robotic simulators

using a basic experiment in robot control using fuzzy logic and evolutionary robotics.

They conclude that V-REP is a more intuitive and user-friendly simulator, and packs

more features. Gazebo is more integrated into ROS framework and is an open source

solution which means it allows for complete control over the simulator. However, it

needs a number of external tools to match up with V-REP functionalities. Also,

Gazebo is more hardware-demanding than V-REP. So, the cognitive scientist should

have a better chance of implementing and validating their cognitive theories using V-

REP than Gazebo.

5.2 EXPERIMENTAL PROTOCOL

The use of the quadcopter has advantages. One of them is regarding the

simplicity of its propulsion and navigation system that consists of four independent

motors and propellers with a fixed pitch. Each pair of opposite propellers rotates in the

same direction to avoid yaw torque during the roll and pitch movement. The system

dynamics are controlled by thrust and torque triggered by every motor unit. The design

of the quadcopter used in this simulation can be seen in Figure 5.1. The additional

module which should be used are the GPS (Global Positioning System) for the

navigation system of the quadcopter, and the wireless module for communication

among the quadcopters as explained in Section 3.1.2.

38

Figure 5.1 The Design of the Quadcopter in the VREP Simulator

In this research, we conduct three experiments divided into three scenarios.

The first scenario, we create two quadcopters, the second scenario has four

quadcopters and for the third scenario, we have eight quadcopters. For every scenario,

the wide of the area is the same which is 24𝑚 × 24𝑚 as shown in Figure 5.2. The area

is composed of the grid cells and the size of each cell is 1𝑚 × 1𝑚. The quadcopter

moves in the middle of the cell. The velocity of all quadcopters is 0.5 𝑚𝑠⁄ . All

quadcopters fly at the altitude of five meters. In this simulation, the effect of wind

disturbance is ignored. At the beginning of the simulation, all quadcopters are given

the information of the environment about the length and the width and the coordinate

of starting point. Thus, the quadcopter uses the GPS to traverse along in the

determined space.

One more property is also equipped to all quadcopters. This property is

provided in the V-REP, named graph. So when they move, they will leave a yellow

mark. This is aimed to see more clearly the final result of the quadcopter’s movement.

39

Figure 5.2 The Design of the Environment in the VREP Simulator

Figure 5.3 shows the starting point of all quadcopters in every scenario for the

expanded square pattern. The starting point of the quadcopters of the expanded square

pattern algorithm is fixed and determined by the user. For two quadcopters and four

quadcopters, the starting point is at the centre of the environment as shown in Figure

5.3[a] and 5.3[b] respectively and for the eight quadcopters, the first four quadcopters

are placed at the centre while the other four is placed between the centre and the

corner of the environment as shown in Figure 5.3[c].

Figure 5.3 The Staring Point of the Expanded Square Pattern Quadcopters: [a] Two

Quadcopters, [b] Four Quadcopters and [c] Eight Quadcopters

40

Figure 5.4 and 5.5 shows the starting point of all quadcopters in every scenario

for the frontier baseline from Yamauchi (1997) and cellular automata from Zelenka

and Kasanicky (2014) respectively. The starting point of both is placed at the edge of

the environment because there is no specific provision in these algorithms according to

Yamauchi (1997) and Zelenka and Kasanicky (2014). It is different from the expanded

square pattern because the quadcopter of the expanded square pattern implements their

exploration algorithm based on cardinal point as explained in Section 4.1 and shown in

Figure 4.2. The placement of the quadcopters is determined by the user. It is applied

for all scenarios.

Figure 5.4 The Starting Point of the Frontier Baseline Quadcopters: [a] Two


Figure 5.5 The Starting Point of the Cellular Automata Quadcopters: [a] Two


41

All scenarios are captured as the result of the experiments. In all experiments,

the simulation is run until the quadcopters finish their exploration. The completion of

the exploration can be defined as a situation where a group of the quadcopter has

covered the determined space. Then, the covered space is calculated based on every

grid cell in the area. The simulation will be terminated when all quadcopters stop the

exploration.

The cooperation of the swarm quadcopters is analysed by observing two

things: the intensity of communication and the exploration and the finishing time

among all members in a group. Therefore, when the quadcopters explore the area, the

act of communication and the finishing time is recorded.

42

CHAPTER SIX

SIMULATION

In this chapter, we will be simulating the exploration algorithm with the expanded

square pattern, the cellular automata from Zelenka and Kasanicky (2014) and the

frontier based approach introduced by Yamauchi (1997). In order to evaluate the

performance of all exploration algorithms, the data from every simulation on the

different number of quadcopters are collected and documented. These data will be

compared, evaluated and analysed in Chapter 7. The various number of swarm robots

is implemented in this simulation which are two, four and eight quadcopters. The data

that will be collected and compared are the simulation time, the spaces covered by the

quadcopters and the time needed for each quadcopter.

6.1 EXPERIMENT I: THE SWARM-BASED EXPLORATION ALGORITHM

WITH THE EXPANDED SQUARE PATTERN

The snapshots of the simulation are shown in Figure 6.1, 6.2 and 6.3 for the different

number of quadcopters which are two, four and eight respectively.

6.1.1 Experiment I: Two Quadcopters

In Figure 6.1, it can be seen that the number of quadcopters that is applied are two and

Figure 6.1[a] shows two quadcopters performing their first expanded square patterns.

After completing the first expanded square pattern, the quadcopters determine their

next location to be explored. Figure 6.1[b] shows that they start to perform their

second expanded square patterns. The interesting part is presented in Figure 6.1[c]

when the quadcopters determine the next location after the second expanded square

43

pattern. It is shown in Figure 6.1[c], since their next locations have been completed at

the first expanded square pattern, both increase their next point coordinate to perform

the third expanded square pattern. The same situation occurs for the next expanded

square pattern as shown in Figure 6.1[d]. In Figure 6.1[e], the quadcopters are going to

do the next exploration algorithm followed by Figure 6.1[f] that shows that the

quadcopters can complete all their exploration tasks. In this scenario, each quadcopter

has to perform six square patterns to cover the whole area.

6.1.2 Experiment I: Four Quadcopters

Figure 6.2 shows the performance of four quadcopters in completing their tasks. In

Figure 6.2[a], they are in the middle of their work for the first expanded square

pattern. Figure 6.2[b] presents four quadcopters that almost finish the first expanded

square pattern. In Figure 6.2[c], the situation is different from the first scenario for the

two quadcopters. The next location for the third expanded square pattern is not

through the area that has been completed. Hence, the quadcopter does not need to

extend its next point coordinate. Figures 6.2[d] and [e] show the situation where the

quadcopters perform their last expanded square pattern. Finally, in Figure 6.2[f], all

quadcopters finish their jobs. In this scenario, each quadcopter has to perform three

expanded square patterns to cover the whole area.

6.1.3 Experiment I: Eight Quadcopters

Figure 6.3 presents the work of eight quadcopters. Figure 6.3[a] gives us a picture of

eight quadcopters starting their exploration. They are dispersed all over the place. In

Figure 6.3[b] and [c], all quadcopters are in the middle of completing their first

expanded square pattern. Figure 6.3[d] describes all quadcopters are going to their

44

next location of expanded square pattern. In Figure 6.3[e], all almost finish their work

and in Figure 6.3[f], all quadcopters have completed their tasks. In this scenario, every

quadcopter must perform only two expanded square patterns to explore the whole area.

Figure 6.1 Two Quadcopter Simulation on Experiment I. The time format is described

in mm:ss. [a] at 01:27, [b] at 02:53, [c] at 05:46, [d] at 11:32, [e] at 17:18 and [f] at

23:03

45

Figure 6.2 Four Quadcopter Simulation on Experiment I. The time format is described


11:42

46

Figure 6.3: Eight Quadcopter Simulation on Experiment I. The time format is

described in mm:ss. [a] at 00:20, [b] at 00:40, [c] at 01:20, [d] at 02:40, [e] at 04:00

and [f] at 05:21

47

In order to evaluate the performance of the simulations in Chapter 7, the data

from three different scenarios on the same environment are collected as shown in

Table 6.1.

Table 6.1 The Duration of the Expanded Square Pattern Algorithm for Different

Number of Quadcopters

Space

(meter2)

Two

Quadcopters

(mm:ss)

Four

Quadcopters

(mm:ss)

Eight

Quadcopters

(mm:ss)

36 01:27 00:44 00:20

72 02:53 01:28 00:40

144 05:46 02:55 01:20

288 11:32 05:51 02:40

432 17:18 08:46 04:00

576 23:03 11:42 05:21

6.2 EXPERIMENT II: THE SWARM-BASED EXPLORATION ALGORITHM

BASED ON THE FRONTIER BASED APPROACH

In this experiment, the exploration based on the frontier based approach introduced by

Yamauchi (1997) is implemented. The snapshots of the simulation are shown in

Figure 6.4, 6.5 and 6.6 for the different number of robots.

6.2.1 Experiment II: Two Quadcopters

First of all, in Figure 6.4[a], it can be seen two quadcopters move to two different

areas for exploration. The first quadcopter goes up and the second quadcopter goes to

the right side of the environment. However, Figure 6.4[b] shows that the first

quadcopter goes down towards the direction of its initial position. And then, in Figure

48

6.4[c], the second quadcopter which went to the right side, moves to the areas

explored by the first quadcopter. After a few minutes, both quadcopters explore the

same area which is in the bottom area. And again, a few minute later, both move to the

same area but now they are in the upper area. It can be seen in Figure 6.4[d] and [e].

Finally, both can finish their exploration and end it in the upper area. In this scenario,

the second quadcopter finishes earlier than the first quadcopter.

6.2.2 Experiment II: Four Quadcopters

Furthermore, Figure 6.5 presents four quadcopters that implement the frontier baseline

algorithm. From Figure 6.5[a], it can be seen that the deployment of four quadcopters

is not spread evenly. Three quadcopters go up and one quadcopter goes right. Figure

6.5[b] also does not show a different situation from the first one. But, in Figure 6.5[c],

one of three quadcopters starts to separate from the other two. Then, four quadcopters

are separated evenly: two and two as seen in Figure 6.5[d]. However, an uneven

distribution happens again. Three quadcopters explore the upper environment and only

one quadcopter explores the bottom area. Finally, Figure 6.5[f] views the end of four

quadcopters’ work.

49

Figure 6.4 Two Quadcopter Simulation on Experiment II. The time format is described


27:39

50

Figure 6.5 Four Quadcopter Simulation on Experiment II. The time format is


and [f] at 13:12

6.2.3 Experiment II: Eight Quadcopters

Figure 6.6 presents the simulation of eight quadcopters. In Figure 6.6[a], eight

quadcopters are distributed evenly and it is different from the previous simulation

51

(four quadcopters). Figure 6.6[b] also shows a good deployment of all quadcopters.

However, from Figure 6.6[c], one quadcopter starts to separate from its group. That

quadcopter explores the right top of the environment alone. Even if this situation

happens, the normal condition of distribution can be conducted again. But, some odd

situation is shown in Figure 6.6[d] where there are two quadcopters that go back to the

explored area. These two quadcopters have wasted time because they explore the same

area again. Figure 6.6[e] shows that there is one quadcopter which still explores while

the others have done their jobs. Lastly, in Figure 6.6[f], it can be seen all quadcopters

get their jobs done.

In order to evaluate the performance of those simulations in Chapter 7, the data

from three different scenarios on the same environment are collected as shown in

Table 6.2.

Table 6.2 The Duration of the Frontier Baseline Algorithm for Different Number of

Quadcopters

Space

(meter2)

Two

Quadcopters

(mm:ss)

Four

Quadcopters

(mm:ss)

Eight

Quadcopters

(mm:ss)

36 01:44 00:50 00:36

72 03:28 01:39 01:11

144 06:55 03:18 02:22

288 13:50 06:36 04:44

432 20:45 09:54 07:06

576 27:39 13:12 09:28

52

Figure 6.6 Eight Quadcopter Simulation on Experiment II. The time format is


and [f] at 09:28

53

6.3 EXPERIMENT III: THE SWARM-BASED EXPLORATION ALGORITHM

WITH THE CELLULAR AUTOMATA

In this experiment, the exploration algorithm with the cellular automata introduced by

Zelenka and Kasanicky (2014) is implemented. The snapshots of the simulation are

shown in Figure 6.9, 6.10 and 6.11 for the different number of robots. For more

information about the cellular automata, in Section 6.3.1, the concept of virtual

mapping in cellular automata is explained.

6.3.1 The Virtual Mapping in the Cellular Automata

In cellular automata, each quadcopter has own world representation (map). World map

of the quadcopter is the cellular grid. The quadcopter uses this grid for the navigation

system and as a memory. Each situation which can be recognized by the quadcopter is

entered into its own virtual map. The initial settings of the virtual map are illustrated in

Figure 6.7.

Figure 6.7 The Virtual Map in the Cellular Automata. The determined space consists

of 5 × 5 cells and the size of the cell is set to 5𝑚 × 5𝑚

In cellular automata, the quadcopter’s method to calculate the covered space is

different from the expanded square pattern and frontier baseline. Here, the use

of 5𝑚 × 5𝑚 grid cell becomes the indicator to say whether the quadcopter has visited

54

the defined area or not. Therefore, if the quadcopter has visited one grid cell, it

assumes that the quadcopter has covered every pixel in the 5𝑚 × 5𝑚 grid cell. Thus, it

can be said that the quadcopter has explored the area based on the virtual map but it is

not accepted based on the covered space explained in Section 5.2. Figure 6.8 shows

the quadcopter in every group that discovered all 5𝑚 × 5𝑚 grid cells.

In Section 6.3.2 to 6.3.4, the result of the quadcopter of cellular automata is

explained. Although the method of this algorithm in calculating the covered space has

different point view, the deployment and cooperation among the quadcopters can still

be examined to see the effect of coordinated movement and the quadcopter’s

effectiveness compared to the other algorithms since it has become problems

mentioned in Section 1.2.

Figure 6.8 The Result of the Cellular Automata in the Virtual Map Viewpoint: [a] Two


6.3.2 Experiment III: Two Quadcopters

Figure 6.9[a] indicates the even deployment of two quadcopters. The first quadcopter

goes up and the second one goes to the right side. But, in Figure 6.9[b], the first

quadcopter goes back to its track. Then, two quadcopters start to move to the upper

direction simultaneously on the different side of the environment. It can be seen in

55

Figure 6.9[c]. The first quadcopter then reaches the upper area earlier than the second

robot and goes back to the lower direction. The second quadcopter also moves to the

center direction of the environment and comes back to the track to go to the upper

environment. All situations are viewed in Figure 6.9[d]. Based on Figure 6.9[e], the

first quadcopter moves to the center of the environment while the second quadcopter

moves to an area that has been explored by the first quadcopter. Here, the repetition of

the exploration is done by the second quadcopter. Finally, Figure 6.9[f] shows two

quadcopters end their exploration. Based on Figure 6.9, the path taken by two

quadcopters have not covered completely the environment compared to the previous

two experiments.

6.3.3 Experiment III: Four Quadcopters

For a different scenario on this simulation, all activities are captured in Figure 6.10.

This simulation starts with the quadcopters that move separately as shown in Figure

6.10[a]. The same situation also happens in Figure 6.10[b]. There is no significant

occurrence until this time. However, after that, the second quadcopter moves back

after it goes up while the other three still maintain their directions. It is shown in

Figure 6.10[c]. Furthermore, the different situation occurs in Figure 6.10[d] when the

fourth quadcopter visits the area covered by the third quadcopter. In Figure 6.10[e],

four quadcopters are seen to explore the same area while there is an area which is still

not covered. Here, the deployment of all quadcopters is not maintained properly. In the

end, Figure 6.10[f] observes that the quadcopter stops after completing its exploration.

56

Figure 6.9 Two Quadcopter Simulation on Experiment III. The time format is


and [f] at 03:18

57

Figure 6.10 Four Quadcopter Simulation on Experiment III. The time format is


and [f] at 04:02

6.3.4 Experiment III: Eight Quadcopters

In addition, for the scenario of the eight quadcopters, at the beginning, the movements

of the quadcopters are so crowded. It is shown in Figure 6.11[a]. The same situation

also appears in Figure 6.11[b] and an uneven separation of quadcopters occurs here.

58

Six quadcopters move to the right direction and only two quadcopters which go up.

However, in Figure 6.11[c], the distribution of the quadcopters have become more

evenly spread. Although until this time the situation is conducted well, Figure 6.11[d]

displays that there are four quadcopters’ paths that interfere with each other. But, after

a few minutes, the situation looks better in Figure 6.11[e] when more quadcopters

reach the top of the environment and the distribution is even. At the end of the

simulation, eight quadcopters stop at different places that are far from each other as

shown in Figure 6.11[f].

In order to evaluate the performance of these simulations in Chapter 7, the data

from the three different scenarios on the same environment are collected as shown in

Table 6.3.

Table 6.3 The Duration of the Cellular Automata Algorithm for Different Number of

Quadcopters

Space

(meter2)

Two

Quadcopters

(mm:ss)

Space

(meter2)

Four

Quadcopters

(mm:ss)

Space

(meter2)

Eight

Quadcopters

(mm:ss)

15 00:13 25 00:15 30 00:10

25 00:25 50 00:30 60 00:21

50 00:50 95 01:05 120 00:40

65 01:39 130 02:01 160 01:23

85 02:29 170 03:06 200 02:05

100 03:18 190 04:02 240 02:46

59

Figure 6.11 Eight Quadcopter Simulation on Experiment III. The time format is


and [f] at 02:46

60

CHAPTER SEVEN

RESULT AND DISCUSSION

The simulation development has taken a period of time which starts from the robot’s

simple navigation system that can only move from one point to another. Finally, the

research reaches the objective which is the development of the exploration algorithm

for a swarm of quadcopters that can implement the expanded square pattern to do the

exploration. In this chapter, the results of experiments that have been carried out in

Chapter 6 are discussed. The data from the different exploration algorithms are

compared. The results of the comparison are analysed and evaluated. All data that

have been collected is transformed into line graph in order to be observed clearly and

easily.

7.1 THE PERFORMANCE OF THE EXPLORATION ALGORITHMS

The performance of the different exploration algorithms is presented in this section.

The statistical test using the Vargha-Delaney A test (Vargha and Delaney, 2000) will

be conducted to observe and quantify the performance of each algorithm.

7.1.1 Experiment I: The Swarm-based Exploration Algorithm with the Expanded

Square Pattern

Three hypotheses for the first experiment are:

H10: The group of four quadcopters explores the environment faster than the

group of two quadcopters.

H20: The group of eight quadcopters explores the environment faster than the

group of four quadcopters.

61



7.1.1.1 Result and Evaluation

As the proposed algorithm in this research, the result of the exploration algorithm with

the expanded square pattern shown in Figure 7.1 gives a satisfactory result. It is

because the number of swarm robots that is implemented in this algorithm affects its

performance. Figure 7.1 shows the first scenario that uses two quadcopters spend more

time compared to the second and third scenario. The second scenario that implements

the four quadcopters spend more time compared to the third scenario. In other words,

it is clear that as the number of quadcopters increase, the time needed to cover the

whole area is decreased.

Figure 7.1 The Comparison of the Different Number of Quadcopter’s Performance in

the Exploration Algorithm with the Expanded Square Pattern

62

For example, in scenario 1 (two quadcopters), to cover the area of 36 m2, the

quadcopters spend 1 minute and 27 seconds. If it is compared to scenario 2 (four

quadcopters), for 1 minute 27 seconds, the quadcopters can cover the area of 72 m2

and to scenario three (eight quadcopters), the quadcopters can cover the area of more

than 144 m2. The other example can be seen in the minute 05:46. Here, in scenario 2,

the quadcopters can only cover the area of 144 m2 while in the scenarios 2 and 3, the

quadcopters can cover a larger area which are 288 m2 and 576 m2 respectively. From

this point of view, it can be seen that a group of eight quadcopters is able to cover four

times faster than a group of two quadcopters and a group of four quadcopters is able to

cover twice as fast than a group of two quadcopters.

7.1.1.2 Analysis: The Vargha-Delaney A Test

The result of the Vargha-Delaney A test is shown in Table 7.1.

Table 7.1 The Magnitude of the Effect Size Indicated by A Test Score: Square Pattern

Simulation Square Pattern Annotation

A test score

(2 and 4 Quads) 0.64 Medium

A test score

(4 and 8 Quads) 0.75 Large

A test score


Analysing the data on simulation time of the proposed algorithm with the

expanded square pattern from two quadcopters compared with four quadcopters using

the A test returned a value of 0.64. Since 0.64 is in the range of up to 64, the A test

indicates a medium difference between the two data sets. The different result is

63

retrieved from the comparison of four quadcopters and eight quadcopters which have a

value of 0.75 that indicates a large difference. Moreover, the result of the comparison

between two quadcopters and eight quadcopters gives a bigger value which is 0.86.

Based on these results, we can conclude that H10 is accepted with a medium difference

and H20 and H30 are accepted with large differences.

From the analysis described in this section, we can summarize that the

magnitude of the difference between the swarm of robots 2, 4 and 8 in terms of time

are large which means that the number of robots to explore an area affects the

effectiveness of the exploration performance.

7.1.2 Experiment II: The Swarm-based Exploration Algorithm based on the

Frontier Based Approach

The three hypotheses for the second experiment are:








In experiment II, the similar graph pattern is shown in Figure 7.2 for the frontier

baseline algorithm. It can also be highlighted that the number of swarm robots

influences their performance. From Figure 7.2, it is obvious that two quadcopters need

64

more time than four quadcopters and eight quadcopters and four quadcopters need

more time than eight quadcopters to cover an area with the same size.


the Exploration Algorithm based on the Frontier Based Approach

For instance, two quadcopters explore the area of 72 m2 and spend 3 minutes

and 28 seconds. But in 3 minutes and 28 seconds, four and eight quadcopters may

cover more than 144 m2. Another instance can be viewed in the thirteenth minute. At

that time, two quadcopters may cover about 288 m2 while four quadcopters almost

finish their exploration and eight quadcopters have already finished their exploration.

Based on this data, it can be proven that eight quadcopters can explore faster than the

four and two quadcopters which are the same result as the proposed algorithm.

However, in the frontier baseline, eight quadcopters can explore only three times faster

65

than two quadcopters where in the expanded square pattern algorithm, eight

quadcopters can explore four times faster than two quadcopters.


The result of the Vargha-Delaney A test is shown in Table 7.2.

Table 7.2 The Magnitude of the Effect Size Indicated by A Test Score: Frontier

Baseline

Simulation Frontier

Baseline Annotation

A test score


A test score


A test score


Furthermore, analysing the simulation time data of the frontier baseline

algorithm from two quadcopters compared with four quadcopters using the A test

returned a value of 0.75. Since 0.75 is above 0.71, the A test indicates a large

difference between the two data sets. The different result is retrieved from the

comparison of four quadcopters and eight quadcopters which are a value of 0.61 that

indicates a medium difference. Moreover, the result of the comparison between two

quadcopters and eight quadcopters gives the same value as the first which is 0.75. That

indicates a large difference. Based on these results, we can conclude that H40 and H60

are accepted with a large difference and H50 is accepted with a medium difference.

66

7.1.3 Experiment III: The Swarm-based Exploration Algorithm with the Cellular

Automata

Three hypotheses for the third experiment are:








If it is compared to the other algorithms, a different arrangement is presented in Figure

7.3. In this line graph, a group of four quadcopters has the longest time to complete the

exploration. It is followed by a group of two quadcopters and lastly a group of eight

quadcopters.

As shown in all algorithms, eight quadcopters can always be the fastest

group. It happens also in this experiment. They only need 2 minutes and 46 seconds to

cover the area of 240 m2. However, in Figure 7.3, the eight quadcopters can only be

two times faster than the four quadcopters which are the slowest group. Unfortunately,

if the eight quadcopter’s performance of the cellular automata is compared to the

expanded square pattern which can be four times faster and the frontier baseline which

can be three times faster, the eight quadcopters of cellular automata has the lowest

efficiency in its performance. Besides that, if it is seen from the swarm perspective, the

number of quadcopters in this algorithm as shown in Figure 7.3 does not affect its

performance. It is proven by looking at the line graph where, although a group of four

67

quadcopters has more quadcopters than a group of two quadcopters, yet it spends a

longer time than a group of two quadcopters to complete the exploration.


the Exploration Algorithm based on the Cellular Automata


The result of the Vargha-Delaney A test is shown in Table 7.3. Moreover, various

results are recognized from the cellular automata simulation. Analysing the simulation

time data of the cellular automata algorithm from two quadcopters compared with four

quadcopters using the A test returned a value of 0.42. Since 0.42 is below 0.56, the A

test indicates a small difference between the two data sets. The different result is

retrieved from the comparison of four quadcopters and eight quadcopters at a value of

0.61 that indicates a medium difference. Moreover, the result of the comparison

between two quadcopters and eight quadcopters give the value 0.58 that indicates a

68

medium difference. Based on these results, we can say that H70 is not accepted

because of a small difference and H80 and H90 are accepted with medium differences.

Table 7.3 The Magnitude of the Effect Size Indicated by A Test Score: Cellular

Automata

Simulation Cellular

Automata Annotation

A test score

(2 and 4 Quads) 0.42 Small

A test score


A test score


7.2 THE PERFORMANCE OF GROUPS OF QUADCOPTERS

The performance of algorithms on the different number of quadcopters are presented

in this section. Three hypothesis for the comparison among the group of quadcopters:

H100: The square pattern can explore the environment faster than the frontier

baseline.

H110: The square pattern can explore the environment faster than the cellular

automata.

H120: The frontier baseline can explore the environment faster than the

cellular automata.

7.2.1 The Group of Two Quadcopters

As the first example, from Figure 7.4, it can be noticed that two quadcopters of the

cellular automata are 1.8 times faster than the two quadcopters of the frontier baseline

and 1.4 times faster than the two quadcopters of the expanded square pattern. But the

69

two quadcopters of the expanded square pattern are 1.2 times faster than the two

quadcopters of the frontier baseline. The different time between two algorithms which

are the expanded square pattern and the frontier baseline and the cellular automata is

not too big.

Figure 7.4 The Comparison of the Algorithm’s Performance for the Group of Two

Quadcopters


The result of the Vargha-Delaney A test for two quadcopters is shown in Table 7.4.

The result of the expanded square pattern compared with the frontier baseline using

the A test returned a value of 0.42. Since 0.42 is below 0.56, the A test indicates a

small difference between the two data sets. The different result is gotten from the

comparison of expanded square pattern and cellular automata which are a value of

0.89 that indicates a large difference. Moreover, the result of the comparison between

70

the frontier baseline and the cellular automata gives a bigger value which is 0.94.

Based on these result, we can conclude that H100 is not accepted because of a small

difference. H110 and H120 is not accepted since the cellular automata is faster than the

others with a large difference although the cellular automata cannot cover the

determined area completely.

Table 7.4 The Magnitude of the Effect Size Indicated by A Test Score: Two

Quadcopters

Simulation Two

Quadcopters Annotation

A test score

(Square Pattern and Frontier Baseline) 0.42 Small

A test score

(Square Pattern and Cellular Automata) 0.89 Large

A test score

(Frontier Baseline and Cellular Automata) 0.94 Large

7.2.2 The Group of Four Quadcopters

The same result is also shown in Figure 7.5 for the comparison of four quadcopters.

The expanded square pattern and the frontier baseline have the similar duration for

their exploration times but the cellular automata has the shortest duration compared to

the other algorithms. Nevertheless, according to Figure 7.5, the cellular automata is

only 1.2 times faster than the expanded square pattern and 1.3 times faster than

frontier baseline. This value is reduced from the comparison of two quadcopters even

if the number of quadcopters is increased (from two to four quadcopters).

71

Figure 7.5 The Comparison of the Algorithm’s Performance for the Group of Four

Quadcopters





small difference between the two data sets. The different result is retrieved from the

comparison of the expanded square pattern and the cellular automata which is a value

of 0.75 that indicates a large difference. Moreover, the result of the comparison

between the frontier baseline and the cellular automata gives a bigger value which is

0.75. Based on all these results, we can conclude that H100 is not accepted because of

a small difference. H110 and H120 are not accepted since the cellular automata is faster

than the others with a large difference although the cellular automata cannot cover the

72

determined area completely. However, the value of the four quadcopters is reduced

from two quadcopters.

Table 7.5 The Magnitude of the Effect Size Indicated by A Test Score: Four

Quadcopters

Simulation Two


A test score


A test score


A test score


7.2.3 The Group of Eight Quadcopters

The final comparison is displayed for the comparison of eight quadcopters. The result

of this comparison is quite interesting because the line pattern that appears is different

from Figure 7.4 and 7.5. Usually, the line of the expanded square pattern and the

frontier baseline is far above the line of the cellular automata but in Figure 7.6, it is

not.

For a group of eight quadcopters, the expanded square pattern is 1.2 times

faster than the cellular automata and 1.7 times faster than the frontier baseline. The

value of this number is reduced again although the number of quadcopters is also

increased. This fact becomes clearer that the effectiveness of the cellular automata’s

performance is not good although it has the shortest duration compared to the others.

73

Figure 7.6 The Comparison of the Algorithm’s Performance for the Group of Eight

Quadcopters


Table 7.6 The Magnitude of the Effect Size Indicated by A Test Score: Eight

Quadcopters

Simulation Two


A test score


A test score


A test score




74


small difference between the two data sets. The different result is retrieved from the

comparison of the expanded square pattern and the cellular automata which is a value

of 0.65 that indicates a large difference. Moreover, the result of the comparison

between the frontier baseline and the cellular automata gives a bigger value which is

0.78. Based on all these results, we can conclude that H100 is not accepted because of

a small difference. H110 and H120 are not accepted since the cellular automata is faster

than others with a large difference although the cellular automata cannot cover the

determined area completely. However, the value of the four quadcopters is also

reduced from two and four quadcopters.

Therefore, from the comparison in terms of the different number of

quadcopters, it can be summarized that although the cellular automata has the shortest

time compared to the others generally but the acceleration value of the quadcopters in

terms of the number of units does not increase significantly compared to the expanded

square pattern and the frontier baseline. Besides that, the shortest time of the cellular

automata in exploration is caused by the covered space that is performed. The

explanation about this matter will be presented in Section 7.3.2.

7.3 THE COVERED SPACE OF ALL ALGORITHMS

This section is divided into two subsections: the comparison of the number of

expanded square pattern that is performed by each quadcopter (algorithm: expanded

square pattern) and the comparison of covered space from all exploration algorithms.

75

7.3.1 The Comparison of the Different Number of Square Pattern

The covered space is one of the results that must be notified in this research.

Especially, for the swarm-based exploration algorithm with the expanded square

pattern, there is one thing that can be recognized as we look at the simulation captures

which is the expanded square pattern itself.

Figure 7.7 shows that for each quadcopter, the group of two quadcopters

performs more expanded square patterns compared to the group of four and eight

quadcopters. For each quadcopter, the group of four quadcopters performs more

expanded square patterns compared to the group of eight quadcopters. It can be said

that as the number of quadcopters is increased, the number of expanded square pattern

that is performed to cover the whole area is less for each quadcopter.

Figure 7.7 The Comparison of Each Scenario in terms of the Number of Square

Pattern Performed for Each Quadcopter

76

For example, in the group of two quadcopters, each quadcopter performs six

expanded square patterns to complete the task while for the group of four and eight

quadcopters, each quadcopter only performs three and two expanded square patterns

respectively. It means that, as the number of the quadcopter is increased, the task for

each quadcopter is reduced. In other words, the cooperation among the quadcopters in

this experiment is proven i.e. the number of quadcopters will affect the performance of

every single one of them. Hence, as the number of quadcopters is increased, the tasks

for each quadcopter decrease.

7.3.2 The Comparison of the Covered Spaces

In this section, the comparisons of the covered space from all algorithms are exposed.

The covered space is related to the time taken for completing exploration. From this

comparison, it can be noticed clearly the reason of the cellular automata has a short

time for its exploration.

If looking at the first comparison i.e. for two quadcopters (see Figure 7.8), it is

visible that the cellular automata does not cover the whole area as it is done by the

frontier baseline and the expanded square pattern. The same thing happens for four

and eight quadcopters that can be seen in Figure 7.9 and 7.10. The frontier baseline

and the expanded square pattern cover the whole area by exploring every pixel of the

area. However, the cellular automata just go around one space and move along to

explore the other side of the environment. By looking at these figures, it is

recognizable that the cellular automata has less explored space compared to the others

although they can finish the exploration earlier than the others according to its

assumption of the covered space as explained in Section 6.3.1. However, it is

77

considered to be not complete according to the expanded square pattern. In order to

observe it more clearly, Figure 7.11 shows the uncovered space marked in red.

Figure 7.8 The Space Comparison for Two Quadcopters: [a] Cellular Automata, [b]

Frontier Baseline and [c] Square Pattern

Figure 7.9 The Space Comparison for Four Quadcopters: [a] Cellular Automata, [b]


Figure 7.10 The Space Comparison for Eight Quadcopters: [a] Cellular Automata, [b]


78

Figure 7.11 The Uncovered Space (Red Colour): [a] Two Quadcopters, [b] Four

Quadcopters and [c] Eight Quadcopters

7.4 THE COOPERATION AMONG THE QUADCOPTERS

After looking at the performance of all algorithms, evaluating the different numbers of

quadcopters’ performance and the covered space of algorithms, the finishing time of

members in every group should be observed. As far as the swarm-based algorithms is

concerned, those three algorithms should consider the finishing time as an important

element in the swarm-based system. It means that since the swarm robot is described

as having a collective behaviour (Dorigo et al., 2014), the cooperation among

quadcopters becomes necessary to be noticed in this context. Therefore, the

cooperation among quadcopters is related to the time that quadcopters start and end

their cooperation with each other.

7.4.1 The Group of Two Quadcopters

The first observation is aimed at the group of two quadcopters. Here, as shown in

Figure 7.12, the expanded square pattern has the smallest time gap compared to the

others which is only 5 seconds. It is followed by cellular automata with 19 seconds

and the frontier baseline with 1 minute and 49 seconds.

79

Figure 7.12 The Comparison of Duration among Group of Two Quadcopters

7.4.2 The Group of Four Quadcopters

The other result is recognized from the group of four quadcopters from Figure 7.13. In

this scenario, the expanded square pattern has a satisfied finishing time. All of its

quadcopters finish their explorations at the same time which is at 11:42. Nonetheless,

the frontier baseline and the cellular automata, have the different result for each of

their quadcopters. For example, in the frontier baseline, the first quadcopter takes the

longest time to finish which is at 13:12 followed by the second, fourth and third

quadcopters. The same result is also shown in the cellular automata where the first

quadcopter takes the longest time which is at 04:02 and is followed by the fourth,

second and third quadcopter.

80

Figure 7.13 The Comparison of Duration among Group of Four Quadcopters

7.4.3 The Group of Eight Quadcopters

Lastly, the result of the group of eight quadcopters is described in Figure 7.14. Here,

the expanded square pattern’s quadcopters have two different finishing time divided

into two groups of time which are at 04:52 and at 05:21 respectively. The first four

quadcopters are at 04:52 and the second four quadcopters are at 05:21. The time gap is

29 seconds. In the frontier baseline, each quadcopter has a different finishing time.

The longest time gap is 4 minutes and 28 seconds. The same thing happens for the

cellular automata where each quadcopter has a different finishing time. The longest

time is 1 minutes and 44 seconds.

81

Figure 7.14 The Comparison of Duration among Group of Eight Quadcopters

7.4.4 The Cooperation of the Quadcopters in terms of Communication

The cooperation of the quadcopters can be seen by observing the communication

generated among members while exploring the determined area. It means that the

number of communication among the quadcopters give an impact on how large the

determined area has been discovered by the quadcopters.

From Table 7.7, it can be seen that the group of two quadcopters generates five

times communication line in order to complete the task. However, as the number of

the quadcopter is increased, the communication among members is reduced. It is

viewed as the group of four quadcopters and eight quadcopters that generate

communication line two and one time(s) respectively.

82

Table 7.7 The Number of Communication and the Covered Space Comparison of the

Expanded Square Pattern

Number of

Communication

The Covered

Space for Two

Quadcopters

The Covered

Space for Four

Quadcopters

The Covered

Space for

Eight

Quadcopters

0 64 m2 128 m2 288 m2

1 128 m2 256 m2 576 m2

2 272 m2 576 m2 -

3 416 m2 - -

4 480 m2 - -

5 576 m2 - -

From Table 7.8, it can be seen that the group of two quadcopters generates

eight times communication line in order to cover 100 m2. However, it can be seen

from Table 7.8, the group of four quadcopters increases the intensity of

communication among the quadcopters up to 21 times to cover 190 m2. However, the

number of the communication line in the group of eight quadcopters is reduced

although the space covered is expanded. It is viewed as the group of eight quadcopters

that generate communication line up to 15 times.

Table 7.8 The Number of Communication and the Covered Space Comparison of the

Cellular Automata

Number of

Communication

The Covered

Space for Two

Quadcopters

The Covered

Space for Four

Quadcopters

The Covered

Space for

Eight

Quadcopters

0 – 4 50 m2 30 m2 60 m2

83

5 – 8 100 m2 60 m2 120 m2

9 – 11 - 90 m2 180 m2

12 – 15 - 120 m2 240 m2

16 – 18 - 150 m2 -

19 - 21 - 190 m2 -

One way to measure the cooperation among the quadcopters is to create a

metric that calculate the effectiveness of the quadcopters in a group using what we call

the effectiveness cooperation (EC). The effectiveness cooperation is an estimate of the

cooperation among the quadcopters to accomplish the task. The EC is defined as a

relationship between the communication lines (CL) generated during the exploration

and the covered space (CS) as the final result retrieved. The communication line is an

important factor of the quadcopters’ cooperation in exploration. A simplistic view of

CL is the number of communication required to interact with the other robots. The

effectiveness cooperation metric is defined as:

𝐸𝐶 = 1 − 𝐶𝐿

𝐶𝐿 + 𝐶𝑆.

Table 7.9 The Measurement of Effectiveness Communication

The Number of

Communication

The Group of

Two

Quadcopters

The Group of

Four

Quadcopters

The Group of

Eight

Quadcopters

The Expanded

Square Pattern 0.991 0.997 0.998

The Cellular

Automata 0.990 0.900 0.940

84

Table 7.9 describes the measurement value of the effectiveness cooperation in

the expanded square pattern and the cellular automata algorithm. In this table, it shows

that the value of the expanded square pattern is bigger than the value of the cellular

automata. Besides that, the increment value of the expanded square pattern is

comparable to the increment of the number of the quadcopters. However, it has a

different pattern in the cellular automata. Here, the biggest value belongs to the group

of two quadcopters and it is followed by the group of eight quadcopters and four

quadcopters. Therefore, from this point of view, the expanded square pattern has the

best result for the quadcopter’s cooperation.

85

CHAPTER EIGHT

CONCLUSION

Exploration is one of the most important elements in the searching activity as a

technique that can be implemented to gather information in an unknown environment.

To collect information, the best result can be achieved if the exploration can be

completed properly. To ensure good exploration, there are two properties that must be

realized: completeness and effectiveness in terms of space and time respectively.

By implementing the expanded square pattern introduced by the National

Search and Rescue Manual, Australia in Authority (2014), we propose a swarm-based

exploration algorithm using the quadcopter for the outdoor environment that can be

applied in a large area. We present the algorithm of exploration with the expanded

square pattern. In addition, we have successfully simulated the proposed algorithm in

VREP simulator and also the swarm-based exploration algorithm with the cellular

automata (Zelenka and Kasanicky, 2014) and the frontier baseline (Yamauchi, 1997).

Therefore, it can be said that two hypotheses in Section 1.3 are accepted. Additionally,

it also answers the first and second question from Section 1.4 about the existing

exploration algorithm and kind of exploration algorithm that can be implemented to

the swarm of quadcopters. Based on the simulation, three algorithms are analysed,

compared and evaluated.

In this research, we look closely into the issue of completeness and

effectiveness. Completeness requires the exploration to cover most of the area and

effectiveness emphasizes the efficiency of the explorer which is the quadcopter to

finish the exploration in minimum time. In the context of the swarm robotic, efficiency

also considers the efficient number of quadcopter for exploration.

86

In regard to the time factor, the cellular automata has the shortest time to finish

the exploration and is followed by the expanded square pattern and the frontier

baseline. However, it happens because the completeness of the covered area of this

algorithm is not as much as the other exploration algorithms. Besides that, based on

the Vargha-Delaney A test, the expanded square pattern has the best result compared

to the others. According to this result, it can be said that as the number of the

quadcopter is increased, the performance of the quadcopter is better. In other words,

the more the quadcopters are added, the faster they can complete the exploration. In

addition, we compare the performance of each group. It shows that the effectiveness of

the expanded square pattern’s performance is the best compare to the other algorithms.

Therefore, based on this result, it can answer the third question in Section 1.4 that is

the expanded square pattern is the most effective swarm-based exploration algorithm

compared to the frontier baseline and the cellular automata.

The completeness factor is also evaluated by comparing the space covered by

each group of quadcopters. The result is derived from captured simulation and shows

that the cellular automata covers the least area compared to the expanded square

pattern and the frontier baseline. Hence, in term of completeness of space, the

expanded square pattern has the better result than the cellular automata.

Finally, as a part of cooperation concerned, the expanded square pattern has the

best result since most of its quadcopters start and end their exploration at the same

time. And then, as the number of the quadcopter is increased, the number of

communication among the quadcopters is decreased. It means that the number of the

quadcopters affects the performance of the swarm quadcopters.

For future work, we expect an add-on and other improvements to the proposed

swarm-based exploration algorithm with the expanded square patterns to be simulated,

87

compared and analysed. The implementation can be done in a real environment, the

altitude of the quadcopter in exploration can be evaluated to look for the best altitude

for exploration and the object identification technique can be applied to this algorithm

to capture the object.

88

REFERENCES

Al Redwan Newaz, A., Pratama, F. A., and Chong, N. Y. (2013). Exploration priority

based heuristic approach to uav path planning. In RO-MAN, 2013 IEEE, pages

521–526. IEEE.

Alvissalim, M. S., Zaman, B., Hafizh, Z., Ma’sum, M., Jati, G., Jatmiko, W., and

Mursanto, P. (2012). Swarm quadrotor robots for telecommunication network

coverage area expansion in disaster area. In SICE Annual Conference (SICE),

2012 Proceedings of, pages 2256–2261. IEEE.

Apvrille, L., Tanzi, T., and Dugelay, J.-L. (2014). Autonomous drones for assisting

rescue services within the context of natural disasters. In General Assembly and

Scientific Symposium (URSI GASS), 2014 XXXIth URSI, pages 1–4. IEEE.

Arkin, R. C. (1998). Behavior-based robotics. MIT press.

Authority, A. A. M. S. (2014). National Search and Rescue Manual, Australia,

volume 1. Australian Maritime Safety Authority (AMSA), 2014 edition.

Bachrach, A., He, R., and Roy, N. (2009). Autonomous flight in unknown indoor

environments. International Journal of Micro Air Vehicles, 1(4):217–228.

Bachrach, A., Prentice, S., He, R., and Roy, N. (2011). Range–robust autonomous

navigation in gps-denied environments. Journal of Field Robotics, 28(5):644–

666.

Balch, T. (1993). Avoiding the past: A simple but effective strategy for reactive

navigation. In Robotics and Automation, 1993. Proceedings., 1993 IEEE

International Conference on, pages 678–685. IEEE.

Bou-Ammar, H., Voos, H., and Ertel, W. (2010). Controller design for quadrotor uavs

using reinforcement learning. In CCA, pages 2130–2135.

Bouabdallah, S., Murrieri, P., and Siegwart, R. (2004). Design and control of an

indoor micro quadrotor. In Robotics and Automation, 2004. Proceedings.

ICRA’04. 2004 IEEE International Conference on, volume 5, pages 4393–

4398. IEEE.

Cantelli, L., Mangiameli, M., Melita, C., and Muscato, G. (2013). Uav/ugv

cooperation for surveying operations in humanitarian demining. In Safety,

Security, and Rescue Robotics (SSRR), 2013 IEEE International Symposium on,

pages 1–6. IEEE.

Cepeda, J. S., Chaimowicz, L., Soto, R., Gordillo, J. L., Alan´ ıs-Reyes, E. A., and

Carrillo-Arce, L. C. (2012). A behavior-based strategy for single and multi-

robot autonomous exploration. Sensors, 12(9):12772–12797.

89

Cesare, K., Skeele, R., Yoo, S.-H., Zhang, Y., and Hollinger, G. (2015). Multi-uav

exploration with limited communication and battery.

Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of

intelligent and Fuzzy systems, 2(3):267–278.

De Hoog, J., Cameron, S., and Visser, A. (2009). Role-based autonomous multi-robot

exploration. In Future Computing, Service Computation, Cognitive, Adaptive,

Content, Patterns, 2009. COMPUTATIONWORLD’09. Computation World:

pages 482–487. IEEE.

Di Felice, M., Trotta, A., Bedogni, L., Chowdhury, K. R., and Bononi, L. ((n.d.)).

Self-organizing aerial mesh networks for emergency communication.

Doniec, A., Bouraqadi, N., Defoort, M., Stinckwich, S., et al. (2009). Distributed

constraint reasoning applied to multi-robot exploration. In Tools with Artificial

Intelligence, 2009. ICTAI’09. 21st International Conference on, pages 159–

166. IEEE.

Dorigo, M., Birattari, M., and Brambilla, M. (2014). Swarm robotics. Scholarpedia,

9(1):1463.

E. Rohmer, S. P. N. Singh, M. F. (2013). V-rep: a versatile and scalable robot

simulation framework. In Proc. of The International Conference on Intelligent

Robots and Systems (IROS).

Engel, J., Sturm, J., and Cremers, D. (2012). Camera-based navigation of a low-cost

quadrocopter. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ


Faigl, J., Krajnık, T., Vonasek, V., and Preucil, L. (2010). Surveillance planning with

localization uncertainty for uavs. In 3rd Israeli Conference on Robotics, Ariel.

Franchi, A., Freda, L., Oriolo, G., and Vendittelli, M. (2007). A randomized strategy

for cooperative robot exploration. In Robotics and Automation, 2007 IEEE


Franchi, A., Freda, L., Oriolo, G., and Vendittelli, M. (2009). The sensor-based

random graph method for cooperative robot exploration. Mechatronics,

IEEE/ASME Transactions on, 14(2):163–175.

Fraundorfer, F., Heng, L., Honegger, D., Lee, G. H., Meier, L., Tanskanen, P., and

Pollefeys, M. (2012). Vision-based autonomous mapping and exploration using

a quadrotor mav. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ


Freda, L. and Oriolo, G. (2005). Frontier-based probabilistic strategies for sensor-

based exploration. In Robotics and Automation, 2005. ICRA 2005. Proceedings

of the 2005 IEEE International Conference on, pages 3881–3887. IEEE.

90

Galvez, R. L., Dadios, E. P., and Bandala, A. A. (2014). Path planning for quadrotor

uav using genetic algorithm. In Humanoid, Nanotechnology, Information

Technology, Communication and Control, Environment and Management

(HNICEM), 2014 International Conference on, pages 1–6. IEEE.

Gonzalez-Banos, H. H. and Latombe, J.-C. (2002). Navigation strategies for exploring

indoor environments. The International Journal of Robotics Research, 21(10-

11):829–848.

Grocholsky, B., Keller, J., Kumar, V., and Pappas, G. (2006). Cooperative air and

ground surveillance. Robotics & Automation Magazine, IEEE, 13(3):16–25.

Gupte, S., Mohandas, P. I. T., and Conrad, J. M. (2012). A survey of quadrotor

unmanned aerial vehicles. In Southeastcon, 2012 Proceedings of IEEE, pages

1–6. IEEE.

Julia, M., Gil, A., Pay ´ a, L., and Reinoso, O. (2008). Local minima detection in

potential field based cooperative multi-robot exploration. International Journal

of Factory Automation, Robotics and Soft Computing, 3.

Julia, M., Reinoso, O., Gil, A., Ballesta, M., and Pay ´ a, L. (2010). A hybrid solution

to the multi-robot integrated exploration problem. Engineering Applications of

Artificial Intelligence, 23(4):473–486.

Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots.

The international journal of robotics research, 5(1):90–98.

Khuswendi, T., Hindersah, H., and Adiprawita, W. (2011). Uav path planning using

potential field and modified receding horizon a* 3d algorithm. In Electrical

Engineering and Informatics (ICEEI), 2011 International Conference on, pages

1–6. IEEE.

Klein, G. and Murray, D. (2007). Parallel tracking and mapping for small ar

workspaces. In Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE

and ACM International Symposium on, pages 225–234. IEEE.

Ko, J., Stewart, B., Fox, D., Konolige, K., and Limketkai, B. (2003). A practical,

decision-theoretic approach to multi-robot mapping and exploration. In

Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003

IEEE/RSJ International Conference on, volume 4, pages 3232–3238. IEEE.

Krajnık, T., Faigl, J., Vonasek, V., Kosnar, K., Kulich, M., and Preucil, L. (2010).

Simple yet stable bearing-only navigation. Journal of Field Robotics,

27(5):511–533.

Krajnik, T., Nitsche, M., Pedre, S., Preucil, L., and Mejail, M. E. (2012). A simple

visual navigation system for an uav. In Systems, Signals and Devices (SSD),

2012 9th International Multi-Conference on, pages 1–6. IEEE.

Krokowicz, T., Gasca, M., Voos, H., and Ucinski, D. (2010). Indoor navigation for

quadrotor uavs using schematic environment maps. In Robotics in Alpe-

91

AdriaDanube Region (RAAD), 2010 IEEE 19th International Workshop on,

pages 457–462. IEEE.

Kumar, V. (1992). Algorithms for constraint-satisfaction problems: A survey. AI

magazine, 13(1):32.

Lau, H. and NSW, A. (2003). Behavioural approach for multi-robot exploration. In

Proceedings of the 2003 Australasian Conference on Robotics and Automation,

Brisbane, Australia.

Lee, D., Kim, H. J., and Sastry, S. (2009). Feedback linearization vs. adaptive sliding

mode control for a quadrotor helicopter. International Journal of control,

Automation and systems, 7(3):419–428.

Lee, D.-T. and Drysdale, III, R. L. (1981). Generalization of voronoi diagrams in the

plane. SIAM Journal on Computing, 10(1):73–87.

Makarenko, A. A., Williams, S. B., Bourgault, F., and Durrant-Whyte, H. F. (2002).

An experiment in integrated exploration. In Intelligent Robots and Systems,

2002. IEEE/RSJ International Conference on, volume 1, pages 534–539. IEEE.

Ma’sum, M. A., Jati, G., Arrofi, M. K., Wibowo, A., Mursanto, P., and Jatmiko, W.

(2013). Autonomous quadcopter swarm robots for object localization and

tracking. In Micro-NanoMechatronics and Human Science (MHS), 2013

International Symposium on, pages 1–6. IEEE.

Mataric, M. J. (1995). Designing and understanding adaptive group behavior. Adaptive

Behavior, 4(1):51–80.

Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.

Mosteo, A. R., Montano, L., and Lagoudakis, M. G. (2008). Multi-robot routing under

limited communication range. In Robotics and Automation, 2008. ICRA 2008.

IEEE International Conference on, pages 1531–1536. IEEE.

Nogueira, Lucas. "Comparative Analysis Between Gazebo and V-REP Robotic

Simulators."

Ok, K., Ansari, S., Gallagher, B., Sica, W., Dellaert, F., and Stilman, M. (2013). Path

planning with uncertainty: Voronoi uncertainty fields. In Robotics and

Automation (ICRA), 2013 IEEE International Conference on, pages 4596–

4601. IEEE.

Patel, A. R., Patel, M. A., and Vyas, D. R. (2012). Modeling and analysis of quadrotor

using sliding mode control. In System Theory (SSST), 2012 44th Southeastern

Symposium on, pages 111–114. IEEE.

Phan, C. and Liu, H. H. (2008). A cooperative uav/ugv platform for wildfire detection

and fighting. In System Simulation and Scientific Computing, 2008. ICSC 2008.

Asia Simulation Conference-7th International Conference on, pages 494–498.

IEEE.

92

Pravitra, C., Chowdhary, G., and Johnson, E. (2011). A compact exploration strategy

for indoor flight vehicles. In Decision and Control and European Control

Conference (CDC-ECC), 2011 50th IEEE Conference on, pages 3572–3577.

IEEE.

Ranft, B., Dugelay, J.-L., and Apvrille, L. (2013). 3d perception for autonomous

navigation of a low-cost mav using minimal landmarks. In International Micro

Air Vehicle Conference and Flight Competition (IMAV2013), Toulouse,

France.

Rengarajan, M. and Anitha, G. (2013). Algorithm development and testing of lowcost

waypoint navigation system. NAVIGATION, 3(2).

Roy, N. and Dudek, G. (2001). Collaborative robot exploration and rendezvous:

Algorithms, performance bounds and observations. Autonomous Robots,

11(2):117–136.

Sang, Y., Cai, Z., and Wang, Y. (2013). An exploration strategy based on frontier and

safe corridor for indoor flight vehicle. In Chinese Automation Congress (CAC),

2013, pages 244–249. IEEE.

Selby, W., Corke, P., and Rus, D. (2011). Autonomous aerial navigation and tracking

of marine animals. In Proceedings of the 2011 Australasian Conference on

Robotics and Automation. Australian Robotics & Automation Association,

pages 1–7.

Shen, S., Michael, N., and Kumar, V. (2012a). Autonomous indoor 3d exploration

with a micro-aerial vehicle. In Robotics and Automation (ICRA), 2012 IEEE


Shen, S., Michael, N., and Kumar, V. (2012b). Stochastic differential equation-based

exploration algorithm for autonomous indoor 3d exploration with a micro-

aerial vehicle. The International Journal of Robotics Research, 31(12):1431–

1444.

Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S., and

Younes, H. (2000). Coordination for multi-robot exploration and mapping. In

AAAI/IAAI, pages 852–858.

Stachniss, C. (2009). Robotic mapping and exploration, volume 55. Springer.

Tanner, H. G. (2007). Switched uav-ugv cooperation scheme for target detection. In

Robotics and Automation, 2007 IEEE International Conference on, pages

3457– 3462. IEEE.

Tisdale, J., Kim, Z., and Hedrick, J. K. (2009). Autonomous uav path planning and

estimation. Robotics & Automation Magazine, IEEE, 16(2):35–42.

Vargha, A. and Delaney, H. D. (2000). A critique and improvement of the CL

common language effect size statistics of McGraw and Wong. Journal of

Educational and Behavioral Statistics, 25(2), 101–132.

93

Xu, R. and Ozguner, U. (2006). Sliding mode control of a quadrotor helicopter. In

Decision and Control, 2006 45th IEEE Conference on, pages 4957–4962.

IEEE.

Yamauchi, B. (1997). A frontier-based approach for autonomous exploration. In

Computational Intelligence in Robotics and Automation, 1997. CIRA’97.,

Proceedings., 1997 IEEE International Symposium on, pages 146–151. IEEE.

Yamauchi, B., Schultz, A., and Adams, W. (1998). Mobile robot exploration and map

building with continuous localization. In Robotics and Automation, 1998.

Proceedings. 1998 IEEE International Conference on, volume 4, pages 3715-

3720. IEEE.

Yuan, J., Huang, Y., Tao, T., and Sun, F. (2010). A cooperative approach for

multirobot area exploration. In Intelligent Robots and Systems (IROS), 2010

IEEE/RSJ International Conference on, pages 1390–1395. IEEE.

Zelenka, J. and Kasanicky, T. (2014). Outdoor uav control and coordination system

supported by biological inspired method. In Robotics in Alpe-Adria-Danube

Region (RAAD), 2014 23rd International Conference on, pages 1–7. IEEE.

Zhou, X. S. and Roumeliotis, S. I. (2006). Multi-robot slam with unknown initial

correspondence: The robot rendezvous case. In Intelligent Robots and Systems,

2006 IEEE/RSJ International Conference on, pages 1785–1792. IEEE.

Zul Azfar, A. and Hazry, D. (2011). A simple approach on implementing imu sensor

fusion in pid controller for stabilizing quadrotor flight control. In Signal

Processing and its Applications (CSPA), 2011 IEEE 7th International

Colloquium on, pages 28–32. IEEE

the development of a swarm-based exploration …staff.iium.edu.my/amelia/mcs(g1426941).pdf · 2016....

Documents