Viewpoint Planning of Robotic Measurement System for Free-Form Surfaces Based on Visibility Cone Space Explorer

Free-form surfaces have been widely used in industrial design and manufacturing. For the requirements of measurement efficiency and precision, robots and optical scanners are applied to measure free-form surface parts increasingly. Due to the complex geometry shapes and occlusions of these parts, how to plan accessible viewpoints of a scanner to achieve the expected coverage rate is a challenging task. This paper presents a novel viewpoint planning method based on the visibility cone space explorer (VP-VCSE) for robotic measurement systems with 7 degrees of freedom (7-DOF). A digital twin for the robotic measurement system is implemented to provide core services for robotic measurement tasks, including sensor simulation and collision detection. To generate initial candidate viewpoints, a novel mesh segmentation algorithm based on the hybrid mixture model is proposed, which is convenient to handle the triangular mesh of the target object. Visibility computation for a target object in given viewpoints is the key to dealing with the occlusion problem. For this purpose, a general visibility model of a structured-light scanner is presented to compute visible areas accurately. In order to reduce occlusions, a visibility cone space explorer is designed to search optimal candidate viewpoints considering inverse kinematics and physical collisions simultaneously. The viewpoint planning problem is formulated as a set covering optimization problem and a next-best-view operator is introduced to improve the efficiency of the genetic algorithm for searching the resultant viewpoint set, guaranteeing the expected coverage rate and data overlap rate. The simulation and experiment results for four different test models show that the proposed algorithm outperforms the existing methods in terms of the uncovered rate and the minimum number of viewpoints.Note to Practitioners—This paper addressed a viewpoint planning problem for the robotic measurement system with a binocular structured light 3D scanner mounted on the end effector of the robot, where a robot and a turntable cooperate to complete the measurement tasks. The goal is to find a minimal number of viewpoints that provides full coverage of the target surfaces. Although many studies have addressed this problem, there is little discussion about strategies to improve coverage rate when the target object has complex occlusions. This paper suggested a valuable practice to construct a visibility cone space to adjust viewpoint to reduce occlusions and improve the overall coverage rate. Simulation and experimental results demonstrated the feasibility and effectiveness of the proposed approach. This paper showed how to deal with various constraints that a feasible viewpoint needs to satisfy in the viewpoint generation, viewpoint adjustment, and viewpoint selection phase. Moreover, this paper provided a solution for developing the visualization, simulation, and interaction of a digital twin for the 7-DOF robotic measurement system. All core services for robotic measurement tasks are implemented based on a set of open source libraries, which provides a convenient learning and research software platform for practitioners. In future research, we will study how to improve the intelligence and cooperation of the robotic measurement system through deep learning or reinforcement learning techniques.