View planning for inspection of sheet metal parts based on Markov decision process

Abstract

The automatic 3D measurement technology based on surface structured light has been widely used in the production of metal sheet parts. Before the measurement, it is necessary to manually plan the robot's measurement viewpoint and path, and each viewpoint needs to take into account the measurement quality of the shape surface and holes at the same time. The planning is very difficult, and manual planning results in long offline debugging time and low measurement efficiency. To address this problem, this paper proposes a view planning method that focuses on holes of metal sheet part. Firstly, the relationship between view angle and hole reconstruction quality is constructed to analyze the influence of view angle on hole measurement. Then, candidate viewpoints, which meet the visibility and hole reconstruction quality requirement, are generated. Finally, the Markov decision process is used to model the coverage planning problem, and the Monte Carlo tree search method is used to generate the shortest viewpoint sequence which meets the coverage ratio requirements. The experimental results show that the method proposed in this paper can realize the complete measurement of complex surface features such as holes, and plan an efficient robotic measurement path.