Reinforcement learning-based stable jump control method for asteroid-exploration quadruped robots

Abstract

Unlike the spherical gravitational field of planets and other large solar system bodies, the gravitational field of asteroids is irregular and weak. It is challenging for a planetary rover to obtain sufficient traction forces in this environment. However, this gravitational environment is suitable for legged robots with jumping ability, but it also imposes higher demands on control methods. Therefore, this study aimed to address the problem of jump control method for asteroid-exploration quadruped robots. As the robot jumps off the surface of an asteroid, it would fly for a certain amount of time because of the low gravitational acceleration. The prolonged flight phase underscores the significance of the robot's take off and attitude control. A model-free stable jumping control method was devised in this study. This method can satisfy the control requirements for takeoff, attitude adjustment, and soft landing by using end-to-end multi-agent reinforcement learning (MARL). MARL is more advantageous than single-agent reinforcement learning in dealing with composite motion control problems under similar observation conditions. A simulated training environment was established, incorporating models of the gravitational field, task partitioning for jumping, and design of reward functions, including jump trajectory planning. The efficacy of the proposed jump control method for a quadruped robot was successfully demonstrated in the gravitational environment of an irregular rod-shaped asteroid, 216 Kleopatra.