lecture 1 introduction no video

8/14/2019 Lecture 1 Introduction No Video

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Introduction to Autonomous Mobile Robots

Prof. Yan Meng

Department of Electrical and Computer EngineeringStevens Institute of Technology


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Course Logistics

Instructor: Yan Meng

Office: Burchard 411

Phone: 201-216-5496

Email:[email protected]

Office hour: Tuesday 3:00pm-5:00pm

Course website:

http://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htm

Homework

Homework will be due one week later after it is assigned

Problem solutions will be posted on-line LATE HOMEWORK WILLNOT BE ACCEPTED AFTER THE SOLUTION IS POSTED

Grading Homework 20% Midterm 20% Final 30% Project 30%
mailto:[email protected]://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmhttp://www.ece.stevens-tech.edu/~ymeng/courses/CPE521/CPE521A.htmmailto:[email protected]


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Course Syllabus

Required Textbook:

Roland Siegwart and Ilah Nourbakhsh, Introduction to Autonomous MobileRobots, MIT Press, April 2004, ISBN# 0-262-19502-X.

Textbook website: http://autonomousmobilerobots.epfl.ch/

Some reading materials and hands out will be distributed in class.

Recommended readings:

George A. Bekey, Autonomous Robots From Biological Inspiration toImplementation and Control,MIT Press, 2005. ISBN 0-262-02578-7.

Robin Murphy, An Introduction to AI Robotics,MIT Press, November 2000.ISBN 0-262-13383-0.

Stefano Nolfi and Dario Floreano, Evolutionary Robotics: The Biology,Intelligence, and Technology of Self-Organizing Machines, MIT Press,2000, ISBN 0-262-14070-5.

Thomas Braunl, Embedded Robotics: Mobile Robot Design andApplications with Embedded Systems, Springer-Verlag Berlin Heidelberg

New York, ISBN 3-540-03436-6.


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Some Robotics Links

http://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companies

http://www.cooper.edu/~mar/robotics_links.htm

http://www.roboticsonline.com/links/ http://www.ieee-ras.org/

http://www.euronet.nl/users/ragman/link_64.html
http://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companieshttp://www.cooper.edu/~mar/robotics_links.htmhttp://www.roboticsonline.com/links/http://www.ieee-ras.org/http://www.euronet.nl/users/ragman/link_64.htmlhttp://www.euronet.nl/users/ragman/link_64.htmlhttp://www.ieee-ras.org/http://www.roboticsonline.com/links/http://www.cooper.edu/~mar/robotics_links.htmhttp://www.ifi.unizh.ch/groups/ailab/links/robotic.html#companies


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Applications of Mobile Robots

Indoor Outdoor

Structured Environments Unstructured Environments

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Autonomous Mobile Robots

The three key questions in Mobile Robotics

Where am I ?

Where am I going ?How do I get there ?

To answer these questions the robot has to have a model of the environment (given or autonomously built)

perceive and analyze the environment

find its position within the environmentplan and execute the movement

Basic tasks: deal with Locomotion and Navigation (Perception,

Localization, Planning and motion generation)

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Control Architectures / Strategies

Control Loop

dynamically changing

no compact model available

many sources of uncertainty

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"Position"Global Map

Perception Motion Control

Cognition

Real WorldEnvironment

Localization

PathEnvironment ModelLocal Map


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Two Approaches

Classical AI(model based navigation)

complete modeling

function based

horizontal

decomposition

New AI(behavior based navigation)

sparse or no modeling

behavior based vertical decomposition

bottom up

Possible Solution Combine Approaches

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Environment Representation

Continuos Metric -> x,y,

Discrete Metric -> metric grid

Discrete Topological -> topological grid

Environment Modeling

Raw sensor data, e.g. laser range data, grayscale images

o large volume of data, low distinctiveness

o makes use of all acquired information

Low level features, e.g. line other geometric features

o medium volume of data, average distinctiveness

o filters out the useful information, still ambiguities

High level features, e.g. doors, a car, the Eiffel tower

o low volume of data, high distinctiveness

o filters out the useful information, few/no ambiguities, not enough information

Environment Representation and Modeling:

The Key for Autonomous Navigation

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Odometry

not applicable

Modified

Environments

expensive,

inflexible

Feature-based

Navigation

still a challenge for

artificial systems

Environment Representation and Modeling: How we do it!

Corridorcrossing

Elevator door

Entrance

Eiffel Tower

Landing at nightHow to find a treasure

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C o u r t e s y K

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Environment Representation: The Map Categories

Recognizable Locations Topological Maps

Metric Topological Maps Fully Metric Maps (continuos ordiscrete)

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Incrementally(dead reckoning)

Odometric or initialsensors (gyro)

not applicable

Modifying the environments(artificial landmarks / beacons)

Inductive or optical tracks (AGV)

Reflectors or bar codes

expensive, inflexible

Methods for Navigation: Approaches with Limitations1

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Methods for Localization: The Quantitative Metric Approach

1. A priori Map: Graph, metric

2. Feature Extraction (e.g. line segments)

3. Matching:

Find correspondence

of features

4. Position Estimation:

e.g. Kalman filter, Markov

representation of uncertainties optimal weighting acc. to a priori statistics

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Methods for Localization: The Quantitative Topological Approach

1. A priori Map: Graphlocally uniquepoints

edges

2. Method for determiningthe local uniqueness

e.g. striking changes on raw data levelor highly distinctive features

3. Library of driving behaviors

e.g. wall or midline following, blind step,enter door, application specific

behaviorsExample: Video-based navigation withnatural landmarks

Courtesy of [Lanser et al. 1996]

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Map Building: How to Establish a Map

1. By Hand

2. Automatically: Map Building

The robot learns its environment

Motivation:

- by hand: hard and costly

- dynamically changing environment

- different look due to different perception

3. Basic Requirements of a Map:

a way to incorporate newly sensed

information into the existing world

model information and procedures for

estimating the robots position

information to dopath planning and

othernavigation task(e.g. obstacleavoidance)

Measure of Quality of a map

topological correctness

metrical correctness

But: Most environments are a mixture of

predictable and unpredictable features hybrid approach

model-based vs. behaviour-based

predictability

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Map Building: The Problems

1. Map Maintaining: Keeping track ofchanges in the environment

e.g. disappearingcupboard

- e.g. measure of belief of eachenvironment feature

2. Representation andReduction of Uncertainty

position of robot -> position of wall

position of wall -> position of robot

probability densities for feature positions additional exploration strategies

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Map Building: Exploration and Graph Construction

1. Exploration

- provides correct topology

- must recognize already visited location

- backtracking for unexplored openings

2. Graph Construction

Where to put the nodes?

Topology-based: at distinctive locations

Metric-based: where features disappear orget visible

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Automatic Guided Vehicles

Newest generation ofAutomatic Guided

Vehicle of VOLVO usedto transport motorblocks from onassembly station to an

other. It is guided by anelectrical wire installedin the floor but it is alsoable to leave the wire toavoid obstacles. Thereare over 4000 AGV onlyat VOLVOs plants.

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Helpmate

HELPMATE is a mobile robot used in hospitals

for transportation tasks. It has various on boardsensors for autonomous navigation in thecorridors. The main sensor for localization is acamera looking to the ceiling. It can detect the

lamps on the ceiling as reference (landmark).http://www.ntplx.net/~helpmate/

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BR700 Cleaning Robot

BR 700 cleaning robotdeveloped and sold byKrcher Inc., Germany

Its navigation system isbased on a verysophisticated sonarsystem and a gyro.http://www.kaercher.de

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ROV Tiburon Underwater Robot

Picture of robot ROV Tiburon for

underwater archaeology(teleoperated)- used by MBARI fordeep-sea research, this UAV providesautonomous hovering capabilities for

the human operator.

Th Kh R b t1


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The Khepera Robot

KHEPERA is a small mobile robot for research and education. It sizes only about 60mm in diameter. Additional modules with cameras, grippers and much more are

available. More then 700 units have already been sold (end of 1998).http://diwww.epfl.ch/lami/robots/K-family/ K-Team.html


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SMARbot Overview

CMU cam2

SONAR sensors

Infrared sensors

Bumper switch

Motors withtank treads

Microprocessorboard

FPGA board

Sensor board

Power board

ZigBee wirelessmodule

Forester Robot1


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Forester Robot

Pulstech developedthe first industrial likewalking robot. It is

designed moving woodout of the forest. Theleg coordination isautomated, butnavigation is still doneby the human operatoron the robot.http://www.plustech.fi/

Robots for Tube Inspection1


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Robots for Tube Inspection

HCHER robots for sewage tube

inspection and reparation. Thesesystems are still fully teleoperated.http://www.haechler.ch

EPFL / SEDIREP: Ventilationinspection robot

A t I d N i ti1


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Autonomous Indoor Navigation (Pygmalion EPFL)

very robust on-the-fly

localization

one of the first systemswith probabilistic sensor

fusion

47 steps,78 meter length,

realistic officeenvironment,

conducted 16 times >

1km overall distance

partially difficult

surfaces (laser),

partially few vertical

edges (vision)

Video is here.

SLAM (Si lt l li ti d i ) b


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SLAM (Simultaneous localization and mapping) by

EPFL

M lti b t SLAM ( CMU)


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Multi-robot SLAM ( CMU)

Tour Guide Robot (N b kh h CMU)1


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Tour-Guide Robot (Nourbakhsh, CMU)

Video is here.

Minerva: a second generation museum tour guide robot


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Minerva: a second-generation museum tour-guide robot

Sojourner, First Robot on Mars1


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Sojourner, First Robot on Mars

The mobile robotSojourner was usedduring the Pathfinder

mission to explorethe mars in summer1997. It was nearlyfully teleoperatedfrom earth. However,some on boardsensors allowed forobstacle detection.http://ranier.oact.hq.

nasa.gov/telerobotics_page/telerobotics.shtm

http://www.youtube.com/watch?v=zZWOGcdC_PI

NASA Rover


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NASA Rover

RoboCup 2006


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RoboCup 2006

Midsize Qualification Video Bremen 2006

A short scene from the final in Osaka 05 against Eigen

Modular Robots


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Modular Robots

Modular Reconfigurable Robotics is an approach to building robots for

various complex tasks. Instead of designing a new and different

mechanical robot for each task, you just build many copies of one simple

module. The module can't do much by itself, but when you connect manyof them together you get a system that can do complicated things. In fact,

a modular robot can even reconfigure itself -- change its shape by moving

its modules around -- to meet the demands of different tasks or differentworking environments.

http://www2.parc.com/spl/projects/modrobots/index.html

Self Reconfig rable Robots


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Self-Reconfigurable Robots

Traditional approaches of building separate robots forseparate tasks may not be cost efficient and appropriate forthose complex tasks in environments that are not humanfriendly.

Reconfigurable robot is modular, multifunctional, andreconfigurable for different tasks at different missionstages.

Challenges: how to coordinate all modules to achieve acommon goal dynamically?

Four layers: hardware, locomotion control, transformcontrol, and cognitive control.

Available Reconfigurable Robots MTRAN( National Institute of Advanced Industrial Science

and Technology, Japan)

SuperBot (Polymorphic Robotics Lab, University of SouthernCalifornia)

Molecube (Cornell University)

Others

M-TRAN (Modular Transformer)


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M-TRAN (Modular Transformer)

http://unit.aist.go.jp/is/dsysd/mtran3/

SuperBot (Polymorphic Robotics Laboratory, USC)


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SuperBot (Polymorphic Robotics Laboratory, USC)

http://www.isi.edu/robots/superbot.htm

CrossCube (Stevens Embedded Systems and Robotics Lab)


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CrossCube (Stevens Embedded Systems and Robotics Lab)

Limitations on locomotion designs andhigh-level control algorithms on theavailable reconfigurable robots

Our objective: to tackle those limitationsand develop a highly flexible locomotionmechanism and more intelligent GRN-based cognitive control algorithm to adaptto dynamic environments and tasks.

CrossCube Hardware and locomotion: lattice-based

robot module that is able to rotate, climband parallel move on other modulessurface

Transform and cognitive control: evolvinggene regulation network(GRN) basedalgorithms.

CrossCube


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CrossCube

Self-reconfigure robot modules tovarious shapes/forms based ondifferent task requirements or

environments. Can self-detect module failures and

self-repair malfunctions byreconfiguration

From homogeneous modules toheterogeneous models

Challenges Flexible, robust, adaptive, reliable,

interactive, integration, etc..

Potential applications Urban search and rescue, security,

space exploration, transportationthrough narrow and complex space,etc.

(video demos)

Biological Inspired Robot: Snake Robot (Tokyo Institute


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g p ( y

of Technology, Shigeo Hirose Group)

On the evening of December 26, 1972, for the first time in the world wesucceeded in producing artificial serpentine movement at a speed ofapproximately 40 cm/sec using the principles of a serpentine movementwhich is the same as actual snakes. The entire length of the device is 2 m,

and it has 20 joints. From http://www-robot.mes.titech.ac.jp/robot/snake/acm3/acm3_e.html

Biological Inspired Robot: Snake Robot (Tokyo Institute


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g p ( y

of Technology, Shigeo Hirose Group)

Raise headSerpentine Propulsion

The system consists perpendicularly connected as a straight chain by the unit

that has batteries, a control board, and actuator of 1 DOF, shell structure hadlightweight and high rigidity.


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Biological Inspired Robots: legged robots


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Centralized versus Distributed Control Laws


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Global Centralized Control

Allow for more coherent team cooperation

Often results in increased communication requirements

The knowledge is computationally costly

Oftentimes all the needed global knowledge is not known

Vulnerable with robot failures and in dynamic environment

Local Distributed Control

Computationally Simple

Handle dynamic environments well Oftentimes unclear as to how

to design local control laws

Must rely on physical sensors

Oftentimes unclear as to how to design local control laws

Biological-Inspired Swarm Robots


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Swarm intelligence is an artificial intelligence (AI) technique based on

and modeled after the emergent, decentralized, self-organized,

collective behavior of insect colonies, bird flocks, and animal herds.

SI Natures Design: Insects


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Organizing highways to and

from their foraging sites by

leaving pheromone trails

Form chains from their own

bodies to create a bridge to pull

and hold leafs together with silk

Division of labour between

major and minor ants

SI Natures Design: Birds


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A flight of ducks use V

formation to reduce air drag and

conserve energy Optimize food searches by using

the eyes of other ducks

Ducks in a flight gain

protectionbetter predator

avoidance odds


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Swarm Robots


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Many of the dangerous, dirty, or Null jobs can be performed more effectively by

groups of robots working together, such as swarms.

Applications

Urbane search and rescue,Surveillance systems, Exploration, Constructions Much more .

Advantages

Parallel processing, cover more areas, coordination, robust and flexible

Main challenges

Adapt their behaviors based on interaction with the environment and

other robots

Become more proficient in their tasks over time

Adapt to new situations as they occur

Coordination and cooperation

Swarm-Bots Project ( Marco Dorigo group in Europe)


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The main objective of the Swarm-bots project is to study a novel approach to the

design and implementation of self-organizing and self-assembling artefacts.

This approach was inspired by the recent studies in swarm intelligence in social

insects and other animal societies. An artefact composed of a number of simpler, insect-like, robots, built out of

relatively cheap components, capable of self-assembling and self-organizing to adapt

to its environment

Swarm Robots MIT/iRobot


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http://people.csail.mit.edu/jamesm/swarm.php

Multi-cellular based Multi-Agent Systems (Stevens

Embedded Systems and Robotics Lab)


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Embedded Systems and Robotics Lab)

Self-organization of large collective systems is a challenging task

Autonomous, adaptable, evolvable, robust, self-repairable, emergent

Suboptimal, non-controllable, non-predictable, not (easily) understandable

Trade-off between global (centralized) and local (distributed) control

Biological development, including cell growth, cell differentiation and morphogenesis,

can be seen as a self-organizing process

Robust to genetic and environmental changes

Use of global and local control

Predictable and relatively understandable

Biological development is under the temporal and spatial control of a gene regulatory

network(GRN) Can we borrow some ideas from developmental biology, in particular the

morphogenesis?

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Simulation Results: Forming shapes


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61The videos can be downloaded from http://www.ece.stevens-tech.edu/~ymeng/lab_home.htm

Preliminary Experimental Results on Multi-Robot

Formation


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Formation

The video demo can be downloaded from http://www.ece.stevens-tech.edu/~ymeng/lab_home.htm

The Honda Walking Robot http://www.honda.co.jp/tech/other/robot.html 1


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http://www.youtube.com/watch?v=kLGk9Q49y7k

Entertainment Robots: Humanoid Robots (SONY)1


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DARPA Grand Challenge


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The DARPA Grand Challenge has been the most significant event for

the robotics community in more than a decade.

A mobile ground robot had to traverse 132 miles of unrehearsed desert

terrain in less than 10 hours.

In 2004, the best robot only made 7.3 miles.

In 2005, Stanford won the challenge and the $2M prize in less than 7hours travel time, and ahead of four other finishers.

Stanford STANLEY (http://robots.stanford.edu/)


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DARPR Grand Challenge 2007


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The Urban Challenge. Teams will compete to build an autonomousvehicle able to complete a 60-mile urban course safely in less than 6hours.

The DARPA Urban Challenge will take place in Victorville, Californiaon November 3, 2007.

"It was an important step to have autonomous ground vehicles thatcan navigate and drive across open and difficult terrain from city tocity. But the next big leap will be an autonomous vehicle that cannavigate and operate in traffic, a far more complex challenge for a

'robotic' driver. So this November we are very excited to be movingfrom the desert to the city with our Urban Challenge."

Dr. Tony Tether, Director, DARPA

Unmanned Maritime System (Senior Design project)


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Point of Contact: M. DeLorme (Center for Maritime Systems)

No of Students: 2

Fields of Interest: Robotics, autonomous systems

Project Sponsor: Office of Naval Research

DESCRIPTION:

The project involves the design, development and demonstration deployment of an unmannedmaritime system (UMS) or systems to perform a task to be specified by the project sponsor.

Students will be responsible for developing the system and deployment specifications based

on independent research and planning. This team will be part of a larger multidisciplinary

team working with students in Mechanical Engineering and Naval Engineering to accomplishthe project goals. Interested students MUST meet with Michael DeLorme

([email protected]) to further discuss the responsibilities and expectations of this project

and to submit a one page resume highlighting their qualifications as related to the proposed

project.

Homework #1


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In order to prepare your project, you may want to search for some

robot simulators from the websites. Please try to find at least two

robot simulators you like and try to use them to see if it is possible for

you to write control programs, such as localization, navigation, multi-robot coordination, on those simulators.

You can find your project partners and build up a group (at most 3

persons for undergraduates, and 2 persons for graduates), or you like todo it individually (more credits).

For the course project, you have two options

Theoretical exploration: real research papers and propose some newapproaches

Building robotic systems, which includes building real robotic systems or

running on a simulation

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