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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
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