Abstract: To enhance task execution efficiency of the Large-Small Combined Manipulator in large-range fine maintenance operations, we propose a multi-layer time-sharing sequential planning method that exploits the dual-arm time-sharing motion characteristics for sequence generation. First, considering cases where task locations exceed the reachable workspace and necessitate adapter switching, a graph-search-based adapter switching strategy is proposed. Second, to reduce motion switching frequency between the manipulators, a cascade point generation method integrating clustering and sampling strategies is proposed. Finally, considering the Large-Small Combined Manipulator kinematic constraints, a stepwise sequence-planning algorithm is designed to produce the order of each manipulator within the Large-Small Combined Manipulator. Simulation experiments demonstrate that the proposed algorithm can autonomously generate adapter switching sequences, cascade points, and action sequences based on Operating points and adapter configurations. Compared with existing algorithms, the proposed method reduces the number of actions by 17.0%, execution time by 17.2%, and joint travel distance by 26.0%, effectively improving the operational efficiency of the Large-Small Combined Manipulator.

Key words: Large-Small Combined Manipulator, sequence planning, graph planning, clustering and sampling, optimization algorithm