A Hybrid Tracking Control Strategy for Nonholonomic Wheeled Mobile Robot Incorporating Deep Reinforcement Learning Approach

Abstract

Tracking control is an essential capability for nonholonomic wheeled mobile robots (NWMR) to achieve autonomous navigation. This paper presents a novel hybrid control strategy combined mode-based control and actor-critic based deep reinforcement learning method. Based on the Lyapunov method, a kinematics control law named given control is obtained with pose errors. Then, the tracking control problem is converted to a finite Markov decision process, in which the defined state contains current tracking errors, given control inputs and one-step errors. After training with deep deterministic policy gradient method, the action named acquired control inputs is capable of compensating the existing errors. Thus, the hybrid control strategy is obtained under velocity constraint, acceleration constraint and bounded uncertainty. A cumulative error is also defined as a criteria to evaluate tracking performance. The comparison results in simulation demonstrate that our proposed method have an obviously advantage on both tracking accuracy and training efficiency.