Active object tracking of free floating space manipulators based on deep reinforcement learning

Abstract

Free-floating space manipulators(FFSM) are more and more widely used in various space tasks, and active object tracking(AOT) is the basis of many missions in space. AOT of FFSM systems has two main difficulties: modeling and control of FFSM systems and tracking motion planning of space manipulators. To deal with these problems, the paper proposed an active object tracking proposal of FFSM systems using deep reinforcement learning(DRL) algorithm, Proximal Policy Optimization(PPO). Our approach is completely data-driven, which avoids the complex modeling process of FFSM and does not require motion planning for space manipulators, which is more concise than traditional algorithms. We trained and tested the algorithm by building a simulation environment in CoppeliaSim, and compared with the resolved motion rate control(RMRC). The results showed that our approach achieved good results in active object tracking of FFSM systems and demonstrated the great potential of DRL algorithms in solving space tasks.