A hierarchical planning framework for self-assembly and self-reconfiguration of autonomous modular space robots

Owing to the extreme conditions of the space environment and the difficulty of external intervention, autonomous modular robotic systems offer a viable solution for constructing structures in space through self-assembly and self-reconfiguration. This paper presents a comprehensive planning system designed for modular robot self-assembly and self-reconfiguration tasks in space. The proposed system consists of two hierarchical layers: a sequential path planning layer and a task and trajectory planning layer. At the sequential path planning layer, the self-assembly and self-reconfiguration tasks are formulated as time-varying online Multi-Agent Path Finding (MAPF) problems, and are effectively solved using the enhanced Time-Expanded Network (TEN) and Minimum-Cost Maximum-Flow (MCMF) algorithm to generate collision-free and efficient sequential paths. At the task and trajectory planning layer, robot tasks are automatically partitioned based on the connection status and motion characteristics, with corresponding dynamic models generated adaptively according to the current task state. A hybrid iterative Linear Quadratic Regulator (iLQR) algorithm is introduced to achieve rapid response and trajectory optimality while satisfying constraints on the joint angles, actuator torques, and interface forces. Simulations using the HexaFlipBot modular robot platform confirmed that the proposed planning approach can efficiently and accurately complete the construction and reconfiguration of typical structures in space.