ErgoSurf：用于未知表面覆盖的遍历控制

中文摘要 AI 辅助

以表面为中心的接触型任务，包括检查、清洁、打磨和抛光等，要求机器人在保持稳定接触的同时系统性地覆盖该表面。遍历控制生成的轨迹会在某一位置停留的时间与任务特定的期望空间分布成正比，从而实现高效的信息采集和覆盖。然而，传统遍历控制方法依赖表面几何的先验知识，或需要视觉传感器输入来预先扫描几何结构，限制了其在未知或动态环境等真实场景中的适用性。本文提出一种新颖的在线遍历控制框架，可实现系统性表面覆盖，同时重构未知表面几何结构。我们采用高斯过程隐式表面（GPIS）模型，在执行过程中从固有触觉传感学习全局表面几何。为实现高效在线规划，我们用观测接触点处切平面采样的点云局部近似表面，并将其迭代拟合到高斯过程中。该近似同时作为目标分布和覆盖分布的采样域。我们采用热扩散类比计算引导遍历探索的势场，将空间覆盖目标转化为平滑的机器人轨迹。我们通过仿真和真实机器人实验验证了该框架，证实其可同时实现遍历覆盖和在线表面几何学习，重构误差接近真实值。

英文摘要

Contact-centric tasks on surfaces, ranging from inspection and cleaning to sanding and polishing, require robots to systematically cover the surface while maintaining stable contact. Ergodic control generates trajectories that spend time at a location proportional to a desired, task-specific spatial distribution, enabling efficient information gathering and coverage. However, traditional ergodic control methods rely on prior knowledge of surface geometry or require a vision sensory input to scan the geometry beforehand, limiting their applicability in real-world scenarios with unknown or dynamic environments. This paper introduces a novel online ergodic control framework that achieves systematic surface coverage while simultaneously reconstructing unknown surface geometry. We employ a Gaussian Process Implicit Surface (GPIS) model that learns global surface geometry from intrinsic tactile sensing during execution. For efficient online planning, we approximate the surface locally using point clouds sampled from tangent planes at observed contact points and iteratively fit them to the Gaussian Process. This approximation simultaneously serves as the sampling domain for both the target and the coverage distributions. We employ a heat-diffusion analogy to compute potential fields that guide ergodic exploration, translating spatial coverage objectives into smooth robot trajectories. We demonstrate our framework through simulation and real-robot experiments, validating simultaneous ergodic coverage and online surface geometry learning with reconstruction errors approaching the ground truth.

发表机构

• German Aerospace Center (DLR)（德国航空航天中心）
• Technical University of Munich (TUM)（慕尼黑工业大学）

机构由 AI 辅助整理，请以论文原文为准。