提出新方法，高效评估机器人在复杂环境下的表面全覆盖可行性。
Constraint Manifold Exploration for Efficient Continuous Coverage Estimation
- 构建扩展配置空间，融合工具位置与姿态约束。
- 基于连续性采样策略，实现复杂曲面全覆盖的快速估计。
- 适用于多关节机械臂在打磨喷漆等场景中的轨迹可行性分析。
许多自动化制造过程依赖工业机器人沿工件表面移动专用工具。在研磨、抛光、喷涂或检测等应用中，需确保工具始终垂直于表面并完全覆盖工件。尽管已有轨迹生成方法，但缺乏对完整表面覆盖可行性的有效分析手段。本文提出一种基于采样的连续覆盖估计方法，通过探索配置空间中的可达表面区域来实现。定义了一个扩展的环境配置空间，以表征工具的位置与姿态约束。采用基于连续性的方法，结合两种不同的采样策略进行探索。在不同运动学结构和环境下的全面评估验证了该方法在复杂环境中准确、高效计算表面覆盖率的能力。
原文摘要 · Abstract (English)
Many automated manufacturing processes rely on industrial robot arms to move process-specific tools along workpiece surfaces. In applications like grinding, sanding, spray painting, or inspection, they need to cover a workpiece fully while keeping their tools perpendicular to its surface. While there are approaches to generate trajectories for these applications, there are no sufficient methods for analyzing the feasibility of full surface coverage. This work proposes a sampling-based approach for continuous coverage estimation that explores reachable surface regions in the configuration space. We define an extended ambient configuration space that allows for the representation of tool position and orientation constraints. A continuation-based approach is used to explore it using two different sampling strategies. A thorough evaluation across different kinematics and environments analyzes their runtime and efficiency. This validates our ability to accurately and efficiently calculate surface coverage for complex surfaces in complicated environments.
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