Neural Computation for Robot Path Planning

H. Meng and S. Dost (UK)


Neural networks, Robotics, Pathplanning.


This paper describes an improvement over Park’s original neural path planning algorithm. The Park’s network needs to set the weights by hand and have serious local minimaproblems. In our network the weights are found using back-propagation. To improve Park’s method the fast simulated annealing schedules were employed in the path finding procedure to avoid local minima. The improved algorithm provides massive parallelism in computation and good performance in finding path.

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