Efficient Robotic 3D Measurement Through Multi-DoF Reinforcement Learning for Continuous Viewpoint Planning

Abstract

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.