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HEURISTICS BASED PATH PLANNING FOMOBILE ROBOT

S.DINESH

(13W05)

Guided byDr.S.SARAVANA PERUMAL

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Mobile robots are used in wide range of real world applications

Ware house operations

Path explorer

Rescue mission

Major issue is path planning

Classical and heuristics methods

Main objective shortest & collision free path .

Degree of riskiness, smoothness of the path, computation time , als

Introduction

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Classification of path planning methods

Traditional method

Global visibilitygraph algorithm

Potential field

Cell

decomposition

Heuristic me

Particle swamOptimisation

Ant colony

Neural networ

Genetic

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Either optimal or no feasible solution.

Expensive computation.

Trapped in local minima .

Brittle in uncertain environment .

Heuristic method

Classical method

Near optimal solution

Reliable in dynamic environment

Less time consuming

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Objective

To propose a shortest , smooth and collision free path for a mobilein a uncertain environment where new obstacles or positionobstacles changes frequently using heuristic algorithm.

Minimum degree of riskiness.

Computation time .

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Literature review

Adem & Mehmet (2012,gives new approach to dynamic path planning by introdu

mutation operator in GA. This proposed mutation method simultaneously che

free nodes close to mutation node instead of randomly selecting a node one by on

accepts the node according to the fitness value of total path instead of the directi

through the mutated node.

Hong et al gives a improved GApresents an effective and accurate fitness fun

genetic operators of conventional genetic algorithms and proposes a new genetic

operator. Moreover, the improved GA, compared with conventional Gas, is bette

the problem of local optimum and has an accelerated convergence rate.

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Literature review

Yong et all (2013) swarm optimizationproposes a multi-objective path plannin

on particle swarm optimization for robot navigation in unknown environment. F

membership function is defined to evaluate the risk degree of path. Considering

merits: the risk degree and the distance of path, the path planning problem with u

sources.

Abdulmuttalib (2013)proposed a novel method for robot navigation in dynamic

referred to as Visibility Binary Tree algorithm. To plan the path of the robot, the

on the construction of the set of all complete paths between robot and target takin

inner and outer visible tangents between robot and circular obstacles. The paths

create a visibilit binar tree on to of which an al orithm for shortest ath is ru

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Research Gap

To extend the path planning algorithm to 3-D environment with vobstacles.

To extend the algorithm to dynamic environment andenvironment.

To improve the smoothness of the path and improve the perform

algorithm by reducing the computation time.

To avoid bottleneck like time for graph construction and searcpath.

To find the with collision free path with minimum risk degreedistance.

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Phase 1 Activity chat4 months

Literature review

Proposing methodology

&solution

Identify the isand problem

SepSubmit the ph

Repor

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Particle swarm optimisation

Improved genetic algorithm

GA with Co-evolution

Visibility binary tree algorithm

High resample times Hig

Average solution timeis more

Known environment

Graph constructionand search shortestpath

Tc

Exe

La

Bottle neck

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References

Adem Tuncer , Mehmet Yildirim (2012) Dynamicpath planning of mobile robots with im

algorithm ,computers & Electrical engineering volume 38, Issue 6 ,November 2012, Page

Yong Zhang n, Dun-weiGong n, Jian-huaZhang (2013) Robot path planning in uncertMulti-objective particle swarm optimization,Neurocomputing volume 103, 1 March 2013

Abdulmuttalib Turky Rashid (2013) Path planning with obstacle avoidance based o

algorithm Robotics & Autonomous systems , volume 61, Issue 12, Pages 1440-1449.

Hong Qu a,n, KeXing a, TakacsAlexander (2013) An improved genetic algorithm with c

for global path planning of multiple mobile robots,Neurocomputing , volume 120, Pages

Atyabi, A & Powers, D M 2013, 'Review of classical and heuristic-based navigation and pa

approaches', International Journal of Advancements in Computing Technology (IJACT), 5(

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Thank you