仅靠接触状态估计复杂零件插入姿态，成功率提升6倍。
Accurate Pose Estimation Using Contact Manifold Sampling for Safe Peg-in-Hole Insertion of Complex Geometries
- 用6秒在线运动构建接触状态子流形，映射到离线接触流形。
- 在0.1~1.0mm间隙下成功率达96.7%，比传统方法高6倍。
- 显著降低平均力矩，适合精密装配场景。
机器人对复杂非凸几何体进行紧密间隙装配仍具挑战性，需精确的状态估计以实现成功插入。本文提出一种仅依赖接触状态的全SE(3)姿态估计新框架。通过仅6秒在线执行的原始运动，构建接触状态的在线子流形，并将其映射至离线接触流形以实现精确姿态估计。我们证明，缺乏此类状态估计会导致机器人卡死和过大力作用，可能造成损伤。在五个具有工业相关性的复杂几何体上评估，间隙为0.1至1.0毫米，取得96.7%的成功率，较无状态估计的原始插入方法提高6倍。此外，我们分析了插入力与总时间，结果表明该方法显著降低平均力矩，实现更安全高效的装配。
原文摘要 · Abstract (English)
Robotic assembly of complex, non-convex geometries with tight clearances remains a challenging problem, demanding precise state estimation for successful insertion. In this work, we propose a novel framework that relies solely on contact states to estimate the full SE(3) pose of a peg relative to a hole. Our method constructs an online submanifold of contact states through primitive motions with just 6 seconds of online execution, subsequently mapping it to an offline contact manifold for precise pose estimation. We demonstrate that without such state estimation, robots risk jamming and excessive force application, potentially causing damage. We evaluate our approach on five industrially relevant, complex geometries with 0.1 to 1.0 mm clearances, achieving a 96.7% success rate - a 6x improvement over primitive-based insertion without state estimation. Additionally, we analyze insertion forces, and overall insertion times, showing our method significantly reduces the average wrench, enabling safer and more efficient assembly.
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