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

TL;DR

This paper presents a novel learning system combining trajectory planning and target estimation modules for free-float dual-arm space manipulators to effectively track non-cooperative, spinning space objects with unknown motion characteristics.

Contribution

It introduces a two-module learning system that integrates multi-target trajectory planning with point cloud-based motion property estimation for non-cooperative objects.

Findings

Successfully tracks spinning objects with unknown motion.

Demonstrates scalability and generalization of the approach.

Outperforms traditional methods in dynamic target tracking.

Abstract

Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods have shown a more promising sign in the trajectory planning of space 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 for motion planning of free-float dual-arm space manipulator (FFDASM) towards non-cooperative objects. Specifically, our method consists of two modules. Module I realizes the multi-target trajectory planning for two end-effectors within a large target space. Next, Module II takes as input the point clouds of the non-cooperative object to estimate the motional property, and then can predict the position of target points on an non-cooperative object. We leveraged the combination of Module I and Module II to track target points on a spinning object with unknown regularity successfully. Furthermore, the experiments also demonstrate the scalability and generalization of our learning system.