Research On Reinforcement Learning Methods For Navigation And Control Of Autonoumous Mobile Robots

To improve the control performance and adaptive ability in unknown ervironments with machine learning, especially reinforcement learning, is an imorptant research topic in the navigation and control of mobile robots. Under the support of the National Natural Science Foundation Project -- Research on kernel-based reinfrocement learning and approximate dynamic planning method,this paper studied the performance evaluation of approximate policy iteration (API) algorithms, parameters optimization of kernel function in kernel-based least-squares policy iteration (KLSPI), autonomous obstacle avoidance with API and learning control of longitudinal velocity of autonomous vehicles.The main contributions and innovations of this paper can be summarized as follows:1,API algorithms were tested in their performances. By carrying out experiments and analyzing their results, the much better performance of API algorithm was validated. It is demonstratred that KLSPI can have better performance when solving sequential decision-making problems with smooth value functions. It is verified that whether is the sequential decision-making problems with smooth value functions or not will play an important role in the performance of approximate policy iteration. Then, a new sample sparseness method which is analysed byε-neigthbor is proposed based on KLSPI, and furthermore, a width optimization method of kernel function based on the Bellman error gradient descent was proposed. Simulation results indicated the efficiency of the method.2,A Markov Decision Processe (MDP) model was given for autonomous obstacle avoidance of mobile robots. As a result, a new autonomous obstacle avoidance method, combining the API algorithm with the rolling window planning method, was proposed for mobile robots in unknown environment. Furthermore, the generalization and adaptation of the proposed method was tested in simulation, and the reliability of the two API-based control methods for autonomous obstacle avoidance was analyzed comparatively. It was indicated that the KLSPI-based autonomous obstacle avoidance method converged to the near optimal policy more quickly than other methods.3,An analysis was given on the key difficulties of autonomous vehicles, and the development of control systems for autonomous vehicles with learning ability. The motion control of vehicles on high way was modeled as MDPs, and then, an API learning and contrl method was designed to control the longitudinal velocity of vehicles on high way. The simulation results show that the API learning and contrl method can realize high-precision control for the expected velocity,what's more, it lay the foundation for further research on learning control of autonomous vehicles.