Efficient motion planning for high-dimensional robotic systems, such as manipulators and mobile manipulators, is critical for real-time operation and reliable deployment. Although advances in planning algorithms have enhanced scalability to high-dimensional state spaces, these improvements often come at the cost of generating unpredictable, inconsistent motions or requiring excessive computational resources and memory. In this work, we introduce Multi-Graph Search (MGS), a search-based motion planning algorithm that generalizes classical unidirectional and bidirectional search to a multi-graph setting. MGS maintains and incrementally expands multiple implicit graphs over the state space, focusing exploration on high-potential regions while allowing initially disconnected subgraphs to be merged through feasible transitions as the search progresses. We prove that MGS is complete and bounded-suboptimal, and empirically demonstrate its effectiveness on a range of manipulation and mobile manipulation tasks. Demonstrations, benchmarks and code are available at https://multi-graph-search.github.io/.

翻译：针对高维机器人系统（如机械臂与移动机械臂）的高效运动规划，对于实时操作与可靠部署至关重要。尽管规划算法的进展已提升了对高维状态空间的可扩展性，但这些改进往往以生成不可预测、不一致的运动轨迹，或需要过多的计算资源与内存为代价。本文提出多图搜索（Multi-Graph Search, MGS），这是一种基于搜索的运动规划算法，将经典的单向与双向搜索推广至多图场景。MGS在状态空间上维护并增量扩展多个隐式图，将探索聚焦于高潜力区域，同时允许初始不连通的子图随着搜索进程通过可行转移进行合并。我们证明了MGS具有完备性与有界次优性，并通过一系列操作与移动操作任务的实验验证了其有效性。演示、基准测试与代码可在 https://multi-graph-search.github.io/ 获取。