Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

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