# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
###### Abstract  
In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. weld defect detection; wheeled climbing robot; magnetic adsorption; control system; EtherCAT bus Article  
Kaiming Lv 1  
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1 China Yangtze Power Co., Ltd., No. 1 Jianshe Road, Xiko, Xiling District, Yichang 443002, China; [EMAIL_ADDRESS]n (K.L. ); [PERSON]AIL_ADDRESS] (Z.L. ); [PERSON]AIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (Y.M. ); [PERSON]L_ADDRESS] (Y.Z.) Machine Learning, ICML,  
the automation level of on-site maintenance, reducing dependence on manual labor, and improving production efficiency [13]. One of the most promising applications of wall-climbing robots lies in the inspection of damage and weldment failure on the surface of hydropower station steel gates. As a major hydropower-producing country, there exists a vast number of in-service steel gates requiring regular inspection to ensure the safe operation of hydropower stations. Accurate localization of damage and weldment failures is crucial for modern manufacturing, providing a basis for precise quality assessment and targeted repair decisions across various products. However, due to complex backgrounds, a low contrast, weak textures, and class imbalances, achieving accurate localization remains a significant challenge. In recent years, convolutional neural networks (CNNs) [14], owing to their powerful feature extraction capabilities, have made substantial progress and have been widely adopted in defect detection tasks [15]. Given the inherent reliability of permanent magnetic adhesion, it is often preferable to use magnetic adsorption to adhere robots to working surfaces where conditions permit, despite the availability of alternative methods. Some robots utilize track-based locomotion [16; 17; 18], offering good terrain adaptability and limited obstacle-crossing capabilities. However, these designs can cause considerable surface damage during turns. Alternatively, some robots rely on vacuum adsorption [19; 20; 21], which imposes fewer requirements on the material properties of the working surface. Nevertheless, vacuum-adsorbed robots generally have lower load capacities, posing limitations for operations requiring significant payloads. From a biomimetic perspective, insect-inspired wall-climbing robots have demonstrated excellent climbing ability and flexibility [22; 23; 24]. However, their complex wheel-leg mechanisms introduce challenges in control and operational stability. There are two primary limitations for wall-climbing robots used in hydraulic steel gate weld inspection:  
* Autonomy: The robot must autonomously navigate along the weld seams on the hydraulic steel gate surface, utilizing sensors to scan and detect potential defects while minimizing human intervention.