Visual Quality Inspection Planning: A Model-Based Framework for Generating Optimal and Feasible Inspection Poses

Automatic visual quality inspection is pivotal in both computer vision and robotics. It plays a crucial role in manufacturing, where robotic systems are increasingly employed to enhance the speed and efficiency of visual quality assessments. Several inspection planning methodologies have been developed; however, they often address the inspection challenge from a singular perspective of robotics or computer vision. This work introduces a comprehensive approach that synergistically integrates principles from both domains. We present an innovative algorithm designed to generate optimal inspection poses by considering the interplay between the inspected object’s geometry and the kinematics of the robotic setup used for inspection. This is accomplished by taking advantage of the concept of visibility. The effectiveness of our algorithm is demonstrated through simulations and experiments, revealing complete coverage for diverse geometries and materials with a small number of inspection poses. Moreover, we benchmark our framework against box constraints and workspace sampling techniques to generate feasible inspection poses. The results indicate superior performance in achieving extensive coverage and reducing the number of required optimal inspection poses, enhancing the overall inspection process.