Robot path planning for maze navigation

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 nonsmoothness of data. Here the same maze problem is revisited.

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

[2]  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).

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

[4]  Dimitris C. Dracopoulos.  Evolutionary learning algorithms for neural adaptive control , 1997, Perspectives in neural computing.

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

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