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Non-cooperative target tracking plays an important role in many space missions. Recent studies on non-cooperative target tracking are mostly concentrated in the field of passive target tracking. Passive non-cooperative target tracking can effectively be used in the initial phases of a close-range rendezvous manoeuvre. Instead, during the terminal phases of such manoeuvres, active object tracking(AOT) techniques are more convenient. This study propose a general visual servo strategy for the active non-cooperative target tracking of space manipulator. Our proposed method do not need any a priori information about the targets and can be used for space manipulators of any configuration and binocular cameras of any types. Our strategy includes a real-time and accurate position estimation algorithm for non-cooperative targets and a motion planning algorithm combined Position-based visual servo(PBVS) and Image-based visual servo(IBVS). The experiment verified that the proposed strategy well solved the active non-cooperative target tracking task and can be used as a baseline for solving such tracking problems which provides a reference for subsequent studies. [ABSTRACT FROM AUTHOR]

Active non-cooperative target tracking is an important ability for free-floating space manipulator (FFSM) systems. Aiming at the difficulty of pose and posture estimation of non-cooperative targets and the complexity of FFSM planning and control, the paper proposed an end-to-end target tracking algorithm using deep reinforcement learning (DRL) algorithm. The algorithm uses raw camera images as input and directly outputs the manipulator joint velocities. To deal with the partially observable Markov decision processes (POMDP) problem caused by single image input, we combine the soft actor–critic algorithm with recurrent neural networks. Therefore, the velocity information of the target is introduced to transform the task into a fully observable Markov decision-making process (MDP). Our approach is completely data-driven. It avoids the position and posture estimation of non-cooperative target and the complex modeling motion planning process of FFSM. We trained and tested the algorithm by building a simulation environment in CoppeliaSim. The results showed that our approach achieved good results in active non-cooperative target tracking of FFSM systems and demonstrated the great potential of DRL algorithms in solving space tasks. [ABSTRACT FROM AUTHOR]