  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.
