针对紧密装配难题，提出基于关键流形引导的采样规划方法。
Manifold-Guided Motion Planning for Tight Assemblies
- 通过关键流形引导采样，聚焦接触点附近的可行配置空间。
- 在旋转装配基准测试中实现100%成功率，首次全自动解决Elk谜题。
- 适合需要高精度装配的机器人操作任务，开源代码可复现。
刚体装配运动规划因几何约束严苛而面临根本挑战。在紧密装配场景中，可行路径常需经过（近）零间隙配置，此时部件间由接触紧密约束。本文提出一种专为紧密装配设计的采样型规划器——关键流形引导快速随机树（CMG-RRT）。核心观察是：在紧密装配中，有效解路径位于或接近关键流形——即至少存在一个部件接触点的构型空间子集。CMG-RRT通过分层划分配置空间，自适应地将采样偏向关键流形邻域，引导探索。在标准间隙假设下，证明了该方法的概率完备性。在具有挑战性的旋转装配基准测试中，所有测试实例均达到100%成功率，包括据作者所知首个全自动解决Elk拆解谜题的方法。开源代码可通过项目主页获取：https://www.cgl.cs.tau.ac.il/projects/tight-assembly-planning。
原文摘要 · Abstract (English)
Motion planning for rigid-body assembly poses a fundamental challenge in robotics due to tight geometric constraints. In such scenarios, feasible motions often require passing through (near-)zero clearance configurations in which the parts are tightly constrained by contact. In this work, we introduce Critical-Manifold Guided RRT (CMG-RRT), a sampling-based planner designed specifically for tight assembly problems. Our key observation is that in tight assemblies, valid solution paths lie on or near a critical manifold: the subset of configuration space consisting of poses with at least one contact point between parts. CMG-RRT guides exploration by adaptively biasing sampling toward neighborhoods of the critical manifold using a hierarchical subdivision of the configuration space. We prove that CMG-RRT is probabilistically complete under standard clearance assumptions. Empirical evaluation on challenging rotational assembly benchmarks demonstrates a 100% success rate across all tested instances, including, to the best of our knowledge, the first fully automatic solution of the Elk disentanglement puzzle. Our open source software is available through our project page: https://www.cgl.cs.tau.ac.il/projects/tight-assembly-planning.
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