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Recent years have seen the emergence of non-cooperative objects in space,like failed satellites and space junk. These objects are usually operated orcollected by free-float dual-arm space manipulators. Thanks to eliminating thedifficulties of modeling and manual parameter-tuning, reinforcement learning(RL) methods have shown a more promising sign in the trajectory planning ofspace manipulators. Although previous studies demonstrate their effectiveness,they cannot be applied in tracking dynamic targets with unknown rotation(non-cooperative objects). In this paper, we proposed a learning system formotion planning of free-float dual-arm space manipulator (FFDASM) towardsnon-cooperative objects. Specifically, our method consists of two modules.Module I realizes the multi-target trajectory planning for two end-effectorswithin a large target space. Next, Module II takes as input the point clouds ofthe non-cooperative object to estimate the motional property, and then canpredict the position of target points on an non-cooperative object. Weleveraged the combination of Module I and Module II to track target points on aspinning object with unknown regularity successfully. Furthermore, theexperiments also demonstrate the scalability and generalization of our learningsystem.

A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object