Self-reconfiguration Strategies for Space-distributed Spacecraft
This addresses the need for reconfigurable, fast-response spacecraft assembly in space missions, but appears incremental as it combines existing learning and planning methods.
The paper tackles the problem of on-orbit spacecraft assembly by proposing a distributed algorithm that uses imitation and reinforcement learning to plan module handling sequences, achieving self-reconfiguration tasks with results demonstrated in Unity3D.
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.