Multi Graph Search for High-Dimensional Robot Motion Planning
🤖 AI Summary
Motion planning for high-dimensional robotic systems—such as manipulator arms and mobile manipulation platforms—often suffers from high computational cost and solution instability. This work proposes the Multi-Graph Search (MGS) algorithm, which introduces, for the first time, a multi-implicit-graph structure into search-based motion planning. By concurrently maintaining and incrementally expanding multiple state subgraphs, MGS dynamically focuses computational effort on high-potential regions and merges subgraphs during search to efficiently discover feasible paths. The method is theoretically guaranteed to be complete and bounded-suboptimal. Experimental results demonstrate that MGS significantly outperforms existing approaches across a variety of high-dimensional tasks, achieving notable advances in computational efficiency, solution consistency, and scalability.
Application Category
📝 Abstract
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/.
Problem
Research questions and friction points this paper is trying to address.
motion planning
high-dimensional systems
robotic manipulation
computational efficiency
search-based planning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Graph Search
motion planning
high-dimensional systems
bounded-suboptimal
search-based planning
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