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. 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：本文提出一种新方法，用于在三维一般曲面上利用网格模型实现近乎完全覆盖且低重叠的覆盖路径（CP）。该方法通过约束质心Voronoi镶嵌（CCVT）将网格模型分割为指定数量的簇，并利用测地度量高效求解簇质心间的最短路径。我们引入一种新的代价函数，在CCVT构建过程中同步实现簇面积的均匀化与三角形法向变化范围的约束。将规划的视点（VP）配置为清洁位姿，采用机械臂执行增材制造中的残粉清除任务。通过将所提出的基于优化的策略集成至运动规划阶段，为每个视点生成候选射线集合，从而解决视点自遮挡问题并确保无碰撞机器人构型。通过覆盖路径规划基准测试与物理实验验证了该方法的有效性。实验表明，本方法可在合理时间内计算包含海量三角形的多种网格模型的覆盖路径与视点配置。