Integrated attitude and landing control for quadruped robots in asteroid landing mission scenarios using reinforcement learning

Abstract

In this investigation, the integrated attitude and landing control scheme for quadruped robots in asteroid landing mission scenario is discussed. Compared with the gravitational field environment of the Earth, the gravitational field near most asteroids has a smaller gravitational acceleration, a non-negligible horizontal acceleration component, and obvious uneven distribution characteristics. In this study, an integrated control method is proposed which focuses on the challenging attitude and landing control schemes of quadruped robots near asteroids by using reinforcement learning in conjunction with an auto-tuned reward function. In the proposed method, attitude adjustment and landing control are trained as a whole. By relying on the trained controller, the quadruped robot can reorient automatically to the most suitable attitude for landing according to gravity and terrain information, thus only relying on the movement of mechanical legs (and not on any additional actuators like reaction wheels) during the entire process. To solve the problem of sparse reward in the process of multi-objective reinforcement learning, an autotuning method of a multi-objective reward function is proposed to improve the training speed. The effectiveness of the proposed landing control method of a quadruped robot is verified near irregular rod-shaped asteroid 216 Kleopatra. The numerical simulation results show that the quadruped robot can adjust reliably its attitude and finally land on irregular terrain without floating and escaping again, even when the gravitational acceleration is unknown and subject to large horizontal components.