This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. The obtained clusters can be used to construct high-quality viewpoints (VP) for visual coverage tasks. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.

翻译：本文提出了一种基于网格模型在三维一般表面上实现低重叠率、近完全覆盖路径（CP）的新方法。该路径通过使用约束质心沃罗诺伊剖分（CCVT）将网格模型分割为指定数量的聚类，并利用测地度量高效地寻找聚类中心之间的最短路径而获得。我们引入了一个新的成本函数，以在构建CCVT时和谐地实现所得聚类的均匀面积以及对三角形法向量变化的约束。得到的聚类可用于为视觉覆盖任务构建高质量的视点（VP）。在此，我们利用所规划的VP作为清洁构型，在增材制造中通过机械臂执行残余粉末的去除。通过将一种基于优化的策略集成到运动规划阶段中，为每个VP寻找一组候选射线，从而解决了VP的自遮挡问题并确保了无碰撞的机器人构型。通过覆盖路径规划基准测试和物理实验验证了所提方法的有效性。结果表明，我们的方法能够在合理时间内计算具有大量三角形的多种网格模型的覆盖路径和视点。