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.