Large-Scale Rapid Growth Reconfiguration in Space: A Decentralized SelfReconfiguration Motion Planning Optimization Strategy for Space Modular SelfReconfigurable Spherical Satellites

Abstract

Large-scale self-reconfiguration (SR) of modular satellites is critical in constructing operating mechanisms and advancing deep space exploration. To achieve rapid and large-scale reconfiguration of space modular self-reconfigurable spherical satellites (SMSRSS), we propose a decentralized growth SR motion planning optimization strategy. This strategy encompasses an SR sequence planning method, a motion space map (MS_map) generation technique, and an enhanced A* path planning algorithm. The SR sequence planning method addresses the NP-complete nature of the problem by employing an improved L-system assignment approach coupled with a self-collision avoidance algorithm to ensure a high gradient of reconfiguration. The MS_map is designed based on the limited observation range and motion characteristics of SMSRSS, aimed at reducing the computational workload of batch path planning feasibility assessments. The enhanced A* path planning algorithm is optimized to minimize reconfiguration transfer steps. Compared with the existing graph-based configuration search algorithms and real-time A* path-searching algorithms, our strategy reduces planning time, minimizes transfer steps, and increases completion rates. In addition, the approach is adapted for general cubic space modular self-reconfigurable satellites to achieve a higher reconfiguration completion rate. The findings demonstrate that our motion planning optimization strategy significantly improves the efficiency of large-scale rapid SR.