Neural robot path planning: The maze problem

The importance of path planning is very significant in the field of robotics. This paper presents the application of multilayer perceptrons to the robot path planning problem, and in particular to the task of maze navigation. Previous published results implied that the training of feedforward multilayered networks failed, because of the non- smoothness of data. Here the path planning problem is reconsidered, and it is shown that multilayer perceptrons are able to learn the task successfully.

[1]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[2]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[3]  X. Pang,et al.  Neural network design for J function approximation in dynamic programming , 1998, adap-org/9806001.

[4]  Stephen Cameron.  Obstacle avoidance and path planning , 1994 .

[5]  P. J. Werbos,et al.  Generalized maze navigation: SRN critics solve what feedforward or Hebbian nets cannot , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[6]  Dimitris C. Dracopoulos,et al.  Evolutionary Learning Algorithms for Neural Adaptive Control , 1997, Perspectives in Neural Computing.