Motion planning algorithm for nonholonomic autonomous underwater vehicle in disturbance using reinforcement learning and teaching method

Abstract

A training algorithm for motion planning of a nonholonomic autonomous underwater vehicle (AUV) in the strong water current is proposed in this paper. The proposed algorithm can be applied in the environment with obstacles placed in arbitrary configuration. In order to realize these functions, the Q-learning and teaching method are introduced and a multilayer structure is proposed. By introducing Q-learning, the motion of the nonholonomic AUV can be suitably treated. Taking advantage of the Baysian net, a motion planning algorithm in the case of an existence of obstacles, is derived automatically from the learned knowledge. The multilayer structure accelerates the learning process. Results of the demonstration by the simulation of control of R-One robot show the high performance of proposed algorithm.