A self-learning reactive navigation method for mobile robots

This paper addresses the navigation problem of mobile robots in unknown environments, where global path planning methods cannot be applied. In such cases, reactive navigation controllers are commonly employed to deal with the uncertainties in motion planning and control. To realize the automatic design of reactive navigation controllers, a self-learning navigation method is proposed in this paper. The self-learning reactive navigation method is based on a Markov decision model of the navigation problem and uses reinforcement learning algorithms to optimize the action policies of mobile robots. Neural networks are employed to approximate value functions in continuous state spaces so that the self-learning navigation controller has good generalization ability and learning efficiency. Simulation results illustrate the effectiveness of the proposed method.