Coverage Path Planning for Robotic Quality Inspection With Control on Measurement Uncertainty

The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views of all the selected viewpoints. Afterward, an enhanced rapidly exploring random tree algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.

I. Introduction

Free-form sheet metal products, such as autobody, aircraft fuselage, and high-speed train body, have complex structures. These products have strict geometric dimensions and tolerance specifications in the forming and multistage assembly processes. Therefore, comprehensive and accurate inspection of the free-form sheet metal parts not only evaluates their manufacturing accuracy but also provides an essential basis to ensure the quality of an assembled product. In recent years, automated robotic inspection systems that are embedded with high-resolution and rapid optical sensors, can provide effective noncontact measurement. Therefore, they have been increasingly used in verifying dimensional and geometrical specifications when assembling various products, including autobody, aircraft, etc. Optical scanners can obtain a large number of point cloud data in a short time. These data capture the sizes, positions, and shapes of products and are ultimately used to calculate dimension errors. Besides, the noncontact measurement avoids damages during the inspection.