用离散流形上的动态运动基元，让机器人学会在复杂曲面上自适应打磨。
MeshDMP: Motion Planning on Discrete Manifolds using Dynamic Movement Primitives
- 基于三角网格的几何信息，扩展动态运动基元以生成表面运动轨迹。
- 通过等距变换调整力项，实现对不同曲面的运动自适应。
- 适用于汽车表面抛光等需要接触的工业任务，仿真与实测均有效。
工业自动化中一个开放难题是：如何可靠完成涉及与复杂工件持续接触的任务，现有方案难以无缝适配工件几何形状。本文提出一种从示范学习的方法，使机械臂通过利用离散流形上的微分算子，从三角网格中提取工件几何信息，将动态运动基元（DMPs）框架扩展至网格表面，生成表面运动轨迹。同时提出一种有效策略，通过引入学习到的扰动项的等距变换，实现运动在不同表面间的自适应。所提出的方法名为MeshDMP，已在仿真与真实实验中验证，在汽车表面抛光等典型工业任务中表现良好。
原文摘要 · Abstract (English)
An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfaces by leveraging differential mathematical operators on discrete manifolds to embed information on the geometry of the workpiece extracted from triangular meshes, and extend the Dynamic Movement Primitives (DMPs) framework to generate motions on the mesh surfaces. We also propose an effective strategy to adapt the motion to different surfaces, by introducing an isometric transformation of the learned forcing term. The resulting approach, namely MeshDMP, is evaluated both in simulation and real experiments, showing promising results in typical industrial automation tasks like car surface polishing.
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