Beijing Forestry University
National Natural Science Funds of China (Grant No. 61703047);the Fundamental Research Funds for the Central Universities(2016ZCQ08)
The motion planning ability of mobile robot are facing a severe challenge with the complex environment and less prior information. It is important to study the motion planning method and theory for mobile robot so that the mobile robot could adapt to complex environment in a long-running and ensure the work security and task efficiency. This article mainly summarized the method based on deep reinforcement learning(DRL), which can deal with the dynamic and complicated obstacles better. The DRL methods,which are based on value and policy, are introduced in this paper respectively. Then, the typical?robot application which work in simulation environment and complex real world environment are analyzed based on DRL. After comparing the advantages and disadvantages in detail, the improvement and optimization direction for DRL method are classified, and the challenges faced by motion planning method are put forward respectively. Finally, the prospects in the field of mobile robot motion planning method with DRL are discussed, which will provide reference for the development of intelligent robots.