Coordinated Control Based on Reinforcement Learning for Dual-Arm Continuum Manipulators in Space Capture Missions

The increasing number of defunct and fragmented spacecraft poses a growing hazard to existing onorbit assets. The redundant continuum manipulator with high flexibility provides dual-arm robotic systems with apparent advantages in active debris removal missions in space. Existing autonomously-coordinated control approaches for dual-arm continuum manipulators require a real-time inverse kinematic solution and a security assurance mechanism for possible collisions, which are difficult to upscale for space debris capture systems with high-speed maneuverability. In this paper, we consider collision avoidance and input saturation control in proposing a multiagent reinforcement learning approach, named the multiagent twin delayed deep deterministic policy gradient (MATD3), to generate a real-time inverse kinematic solution for coordinated manipulators. During the training process, the MATD3 algorithm performs lower overestimation than the multiagent deep deterministic policy gradient (MADDPG) algorithm. Then, a feedback dynamics controller is designed for the continuum manipulators. Under the guidance of the policy networks, each agent can schedule the joint trajectory design online according to the collaborator and target debris information. During the capture operation, a competitive mechanism for the anticollision function is developed through reasonable reward functions to maintain dual arms at a safe distance. Simulation results show that the average accuracy of the proposed approach is 42% higher than that of MADDPG in inverse kinematic trajectory planning. The designed integrated tracking controller can effectively perform capture missions in the simulation environment. Multiagent reinforcement learning shows promise for future onorbit servicing missions.