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.

翻译：许多自动化制造过程依赖工业机械臂沿工件表面移动特定工艺工具。在打磨、抛光、喷涂或检测等应用中，机械臂需要完全覆盖工件，同时保持工具垂直于工件表面。虽然已有方法可为这些应用生成轨迹，但尚无充分手段分析完全表面覆盖的可行性。本研究提出一种基于采样的连续覆盖估计方法，通过在构型空间中探索可达表面区域。我们定义了一个扩展的环境构型空间，用以表示工具位置和方向约束。采用基于延拓的方法，结合两种不同采样策略对该空间进行探索。通过对不同运动学结构和环境进行全面评估，分析了算法的运行时间和效率。这验证了我们能够准确高效地计算复杂环境中复杂曲面的表面覆盖率。