Overview of Robotic Path Planning

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior Rahul Kala

Overview of Robotic Path Overview of Robotic Path PlanningPlanning

Rahul Kala,

Department of Information Technology

Indian Institute of Information Technology and Management Gwalior

http://students.iiitm.ac.in/~ipg_200545/

[email protected],

[email protected]



PublicationsPublications Kala, Rahul, Shukla, Anupam & Tiwari, Ritu (2009), Robotic Path Planning using

Multi Neuron Heuristic Search, Proceedings of the ACM 2009 International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2009, pp 1318-1323, Seoul, Korea

Kala, Rahul, Shukla, Anupam, Tiwari, Ritu, Roongta, Sourabh & Janghel, RR (2009) Mobile Robot Navigation Control in Moving Obstacle Environment using Genetic Algorithm, Artificial Neural Networks and A* Algorithm, Proceedings of the IEEE World Congress on Computer Science and Information Engineering, CSIE 2009, pp 705-713, Los Angeles/Anaheim, USA

Shukla, Anupam, Tiwari, Ritu & Kala, Rahul (2008), Mobile Robot Navigation Control in Moving Obstacle Environment using A* Algorithm, Proceedings of the International conference on Artificial Neural Networks in Engineering, ANNIE 2008, Intelligent Systems Engineering Systems through Artificial Neural Networks, ASME Publications, Vol. 18, pp 113-120, Nov 2008

Shukla, Anupam, Tiwari, Ritu, Kala, Rahul (2009) Mobile Robot Navigation Control in Moving Obstacle Environment using Genetic Algorithms and Artificial Neural Networks, International Journal of Artificial Intelligence and Computational Research, Vol. 1, No. 1, pp 1-12, June 2009

MOBILE ROBOT MOBILE ROBOT PATH PLANNING PATH PLANNING

Research in



The Problem Statement The Problem Statement Inputs◦Robotic Map◦Location of Obstacles◦Static and Dynamic

Constraints◦Time Constraints◦Dimensionality of Map◦Static and Dynamic

EnvironmentOutput

• Path P such that no collision occurs



Existing Algorithms:Existing Algorithms:

A* AlgorithmArtificial Neural

NetworksGenetic AlgorithmsMulti-Neuron Heuristic

Search (MNHS)Neuro-Fuzzy

Self designed Algorithms:Self designed Algorithms:

Multi Algorithms/Hierarchical Algorithms

Hierarchal MNHSHierarchical A* with

Genetically Optimized Fuzzy Inference System

Evolving Robotic Path with Genetically Optimized Fuzzy Inference System

Swarm Intelligence etc

Problem Implementation by



A* AlgorithmA* Algorithm“I believe this is this way takes me

shortest to the destination…. Lets give it a try”

“Hey I got struck… I’ll choose another path”

Add all possible moves in an open list.Make the best move as per open list

statusAdd all executed moves in the closed list



ResultsResults



ANN with Back Propagation ANN with Back Propagation AlgorithmAlgorithm

“Whenever this type of situation arrives… Always make this move”

“Hey rules failed… I’m struck… OK make random moves till you are out”

Frame input/output pairs for every situation comprising of robot position, goal position and environment

Learn these and use them in decision makingMake random moves when position

deteriorates



ResultsResults



Genetic AlgorithmsGenetic Algorithms“Show me some random paths so that I may

decide”

“OK this path is the best to go till a point and this path the best for the other part of the

journey… Let me mix them both…”

Generate random complete and incomplete solutions: source to nowhere, nowhere to goal and source to goal

Try to mix paths to attain optimalityGenerate random paths between needed

points



Graphical Genetic Graphical Genetic OperatorsOperators

Crossover

Mutation



ResultsResults



MNHS AlgorithmMNHS Algorithm“I believe this is this way takes me shortest

to the destination…. Lets give it a try”

“But in the process I may get struck… Lets walk a few steps on bad paths as well”

Add all possible moves in an open list.Make the a range of moves best to

worst as per open list statusAdd all executed moves in the closed

list



Basic Concept of MNHSBasic Concept of MNHS



ResultsResults



Simple Algorithm AnalysisSimple Algorithm AnalysisAlgorithm

Advantages Disadvantages

A* Algorithm

Computationally shortest paths in best times.

Works only for small graphs and restricted and quantized moves

Artificial Neural Networks

Can incorporate dynamic changes in environment. Computationally very fast

Only works for simple graphs. Gets trapped in complex graphs. Path not optimal. Restricted Moves.

Genetic Algorithms

Work for larger and complex graph.

Computationally expensive.

MNHS Low computation and best path lengths in complex and uncertain graphs

Works only for small graphs and restricted and quantized moves

Neuro-Fuzzy Algorithms

Can incorporate dynamic changes in environment. Computationally very fast

Only works for simple graphs. Gets trapped in complex graphs. Path not optimal.

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The Big ObservationThe Big Observation

and hence the game starts…and hence the game starts…



Thank YouThank You

Overview of Robotic Path Planning

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