Journal of Astronautics ›› 2022, Vol. 43 ›› Issue (9): 1176-1185.doi: 10.3873/j.issn.1000-1328.2022.09.005

Previous Articles     Next Articles

Design and Experiment of Complex Terrain Adaptive Robot Based on Deep Reinforcement Learning

YANG Dun, YANG Shuai, YU Yang, WANG Qi   

  1. School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
  • Received:2022-02-12 Revised:2022-06-12 Online:2022-09-15 Published:2022-09-15

Abstract: For the lightweight autonomous exploration mission of planetary surface, a sea urchin like twelve leg spherical robot is proposed based on the structural bionic idea. It has the potential to autonomously change the structure to fit the complex terrain, and can realize omnidirectional motion without overturning and high fault tolerance. Based on the data driven method, a data efficient model free reinforcement learning motion strategy is designed for the robot, which can realize zero to one gait training and deployment without prior knowledge and rapid deployment of the physical prototype of gait. Through the simulation experiments on flat ground and unstructured terrain, it is verified that the trained robot has the ability to move autonomously and adapt to unstructured terrain. By comparing with the commonly used benchmark strategies, it is proved that the proposed strategy has the advantages of high training efficiency and good robustness. Finally, a prototype is developed to verify the dynamic feasibility of the gait generated in the simulation environment in the real physical environment.

Key words: Bionic robots, Reinforcement learning, Complex terrain, Autonomous movement strategies, Planetary exploration

CLC Number: