Gaussian process regression based inspection robot for predicting and locating pipeline anticorrosion coating defects

Abstract

The Direct Current Voltage Gradient (DCVG) technology is adept at identifying defects and corrosion issues within the anti-corrosion layer of buried pipelines by measuring changes in voltage gradient above the ground. Its widespread adoption in the field of anti-corrosion layer defect detection for its high precision and accuracy. However, the current DCVG inspection process relies on experienced operators holding electrodes to walk along the pipeline, resulting in a huge workload. To address these challenges, this paper proposes an innovative method that combines Gaussian process regression (GPR) with an intelligent inspection robot for autonomous pipeline anti-corrosion coating defect detection. This method uses environmental data to directly predict the location of defects within a pipeline's anti-corrosion coating. Through incremental learning, the GPR model is trained to be continuously updated based on new samples such as position coordinates and voltage measurements during autonomous inspections. In addition, the intelligent inspection robot operates collaboratively with crawler wheels and UR robotic arms, enhancing motion stability and flexibility in expanding training data sets. Experimental results confirm that the intelligent inspection robot driven by Gaussian process prediction can achieve accurate defect positioning within 25 iteration cycles, with a positioning accuracy within 0.12m. 
This method enhances defect detection accuracy, alleviating operator burden and offering an efficient solution for buried pipeline maintenance.