1. Robotic Path Planning using Multi Neuron Heuristic Search
Rahul Kala,
Department of Information Technology
Indian Institute of Information Technology and Management Gwalior
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 , Seoul, Korea

2. The Problem Inputs Output Constraints 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

3. MNHS Algorithm In all we take α neurons.
We have a list of heuristic costs each corresponding to node seen but waiting to be processed.
We divide the cost range into α ranges equally among them. Each of these neurons is given a particular range.
Each neuron selects the minimum most element of the cost range allotted to it and starts searching.
At one step of each neuron processes its element by searching and expanding the element.
This process is repeated.

4. Path Planning with MNHS
“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 status
Add all executed moves in the closed list

5. Expansion of Nodes I (i-1,j+2) J (i+1,j+2) P (i-2,j+1) B (i-1,j+1) CK
(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) √5

6. Path Generation

7. Results

8. Thank You More from the authors More information at: