Research on the automated control of robots for wiping curved surfaces includes studies on cleaning contaminated areas and controlling the end-effector posture to apply a constant force along curved surfaces. However, such robots should also clean difficult-to-find contaminants such as dust and dirt. One of previous studies has considered motion generation as a generalized traveling salesman problem. In particular, a point cloud is acquired from an RGBD camera, and a large graph is created to represent the surface based on the point cloud. The system then finds an appropriate end-effector posture for each node and sets up multiple coordinate systems. Consequently, an efficient cleaning motion can be generated; however, path generation using the previous study is a highly time-consuming process. Therefore, in this study, a 3D model is used to generate cleaning actions more efficiently. A point cloud is then generated from the mesh data of the 3D model, based on which the surface is represented as a simple graph that can be solved as a traveling salesman problem, thereby reducing the computational time. The optimal end-effector posture is determined based on the shape of the surface and is set as the coordinate system for each node. Finally, experiments are conducted to compare the cleaning results with the previous study results, thereby verifying that results can be obtained in less than one-tenth the computational time required for the previous study results.

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