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The paper introduces Multi-Graph Search (MGS), a novel search-based motion planning algorithm for high-dimensional robotic systems that addresses the limitations of existing methods in terms of motion consistency and computational cost. MGS maintains and expands multiple implicit graphs, focusing exploration on promising regions and merging disconnected subgraphs as needed. The authors prove completeness and bounded suboptimality of MGS and demonstrate its effectiveness on manipulation and mobile manipulation tasks.
Multi-Graph Search offers a more efficient and scalable approach to robot motion planning by intelligently managing multiple search graphs, potentially outperforming traditional unidirectional and bidirectional methods.
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/.