robotic path planning using multi neuron heuristic search

8/14/2019 Robotic Path Planning using Multi Neuron Heuristic Search

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Department of Information TechnologyIndian Institute of Information Technology and Management Gwalior ICIS 09 Nov 24-26, 09

Department of Information TechnologyIndian Institute of Information Technology and Management Gwalior

Robotic Path PlanningRobotic Path Planning

using Multi Neuronusing Multi NeuronHeuristic SearchHeuristic Search

Rahul Kala,Department of Information Technology

Indian Institute of Information Technology andManagement Gwalior

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

[email protected],[email protected]

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


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The ProblemThe Problem Inputs

Robotic Map

Location of Obstacles

Static and Dynamic

Output Path P such that no collision occurs

Constraints

Time Constraints

Dimensionality of Map

Static and Dynamic Environment


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MNHS AlgorithmMNHS Algorithm In all we take neurons.We have a list of heuristic costs each corresponding to

node seen but waiting to be processed.

We divide the cost range into ranges equally amongthem. Each of these neurons is given a particular range.

Each neuron selects the minimum most element of thecost range allotted to it and starts searching.

At one step of each neuron processes its element bysearching and expanding the element.

This process is repeated.


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Path Planning with MNHSPath Planning with MNHS

I believe this is this way takes me shortest to thedestination. Lets give it a try

But in the process I may get struckLets 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 peropen liststatus

Add all executed moves in the closed list


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Expansion of NodesExpansion of Nodes

I(i-1,j+2)

J(i+1,j+2)

P(i-2,j+1)

B(i-1,j+1)

C(i,j+1)

D(i+1,j+1)

K(i+2,j+1)

A(i-1,j)

V(i,j)

E(i+1,j)

O(i-2,j-1)

H(i-1,j-1)

G(i,j-1)

F(i+1,j-1)

L(i+2,j-1)

N(i-1,j-2)

M(i+1,j-2)

Nodes Weights

A(i-1,j), C(i,j+1), E(i+1,j), G(i,j-1) 1

B(i-1,j+1), D(i+1,j+1), F(i+1,j-1), H(i-1,j-1)

2

I(i-1,j+2), J(i+1,j+2), K(i+2,j+1),L(i+2,j-1), M(i+1,j-2), N(i-1,j-2), O(i-2,j-1) and P(i-2,j+1)

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Path GenerationPath Generation


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ResultsResults


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ThankThank

YouYou

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robotic path planning using multi neuron heuristic search

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