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This paper introduces a multi-degree-of-freedom reinforcement learning framework for robotic 3D measurement, addressing limitations of fixed spherical coordinate methods in capturing complex geometries. The framework uses a voxel-based state representation with dynamic ray-traced coverage updates and a dual-objective reward function to balance overlap control and viewpoint minimization. Experimental results on industrial parts show improved overlap regulation and planning efficiency, leading to more accurate 3D reconstructions.
Achieve more accurate and autonomous 3D reconstruction of complex industrial parts by using reinforcement learning to optimize robotic viewpoint planning.
Three-dimensional (3D) measurement is essential for quality control in manufacturing, especially for components with complex geometries. Conventional viewpoint planning methods based on fixed spherical coordinates often fail to capture intricate surfaces, leading to suboptimal reconstructions. To address this, we propose a multi-degree-of-freedom reinforcement learning (RL) framework for continuous viewpoint planning in robotic 3D measurement. The framework introduces three key innovations: (1) a voxel-based state representation with dynamic ray-traced coverage updates; (2) a dual-objective reward that enforces precise overlap control while minimizing the number of viewpoints; and (3) integration of robotic kinematics to guarantee physically feasible scanning. Experiments on industrial parts demonstrate that our method outperforms existing techniques in overlap regulation and planning efficiency, enabling more accurate and autonomous 3D reconstruction for complex geometries.
