Vision-based weld detection and localization for climbing robots in large-scale storage tank inspections

Abstract

<p>Climbing robots are considered an effective solution for inspecting welds on the walls of large storage tanks. For these robotic systems, the efficient and accurate identification and localizing of weld seams are crucial prerequisites for ensuring precise weld seam tracking. In this paper, we investigate machine vision-based algorithms for feature recognition and localization of weld seams on tank walls for inspection of weld seams by a climbing robot. First, we designed the model of the image algorithm to extract the weld features of the tank walls. After extracting the weld features, we propose the novel idea of feature discretization and a Min-outer Rectangle Fitting Algorithm (MRFA), which will achieve the fitting of rectangular features on the discretized weld features. We constructed a mathematical model for calculating the orientation angle of the rectangular box based on the extracted rectangular boxes. This model allows for the real-time and efficient extraction of the rectangular feature’s pose information (x, y, θ). We also propose an efficient method for calculating the curvature of a curve trajectory. The experimental results demonstrate that the proposed image algorithm model and MRFA effectively identify weld features on the storage tank wall surface, while simultaneously achieving high-accuracy feature localization. Positioning errors are maintained within 3 mm for position and 3 degrees for azimuth, indicating both high precision and robustness. Additionally, the algorithm processes each image in approximately 80 milliseconds. The lightweight and efficient design of the proposed model allows it to be easily deployed on a climbing robot for weld seam detection and tracking on tank walls.</p>