多图搜索提升高维机器人运动规划效率与可靠性
Multi Graph Search for High-Dimensional Robot Motion Planning
- 通过维护多个隐式图并行探索，聚焦高潜力区域
- 在复杂任务中实现更快收敛，且保持路径次优性
- 适合需要实时响应的机械臂与移动操作任务
高维机器人系统（如机械臂和移动机械臂）的高效运动规划对实时运行和可靠部署至关重要。尽管规划算法在处理高维状态空间方面取得进展，但常伴随不可预测、不一致的轨迹或过高的计算资源与内存消耗。本文提出多图搜索（Multi-Graph Search, MGS），一种将经典单向与双向搜索推广至多图架构的基于搜索的规划算法。MGS 在状态空间中维护并增量扩展多个隐式图，优先探索高潜力区域，并允许初始不连通的子图在搜索过程中通过可行转移合并。我们证明了MGS的完备性与有界次优性，并在多种操作与移动操作任务中实证其有效性。演示、基准测试及代码已公开于 https://multi-graph-search.github.io/。
原文摘要 · Abstract (English)
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/.
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