Self-reconfiguration Strategies for Space-distributed Spacecraft

TL;DR

This paper introduces a novel distributed on-orbit spacecraft assembly algorithm that uses imitation and reinforcement learning to optimize module handling sequences, enabling efficient self-reconfiguration of spacecraft structures.

Contribution

It presents a new self-reconfiguration strategy combining imitation learning, reinforcement learning, and robotic arm motion planning for space-distributed spacecraft assembly.

Findings

Successful implementation of the strategy in Unity3D simulation.

Effective path planning for robotic arm handling sequences.

Enhanced reconfigurability and response speed of spacecraft.

Abstract

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-orbit self-reconfiguration algorithms play a crucial role in realizing the benefits of distributed spacecraft. This paper adopts the framework of imitation learning combined with reinforcement learning for strategy learning of module handling order. A robot arm motion algorithm is then designed to execute the handling sequence. We achieve the self-reconfiguration handling task by creating a map on the surface of the module, completing the path point planning of the robotic arm using A*. The joint planning of the robotic arm is then accomplished through forward and reverse kinematics. Finally, the results are presented in Unity3D.