Agile robotic inspection of steel structures: A bicycle‐like approach with multisensor integration

• Published in: Journal of field robotics Vol. 41; no. 2; pp. 396 - 419
• Main Authors: Nguyen, Son Thanh, La, Kien Thanh, La, Hung Manh
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.03.2024
• Subjects: Actuators; Adhesive strength; Algorithms; Automation; Bicycles; climbing robots; Configurations; Deep learning; Empirical analysis; Ferromagnetism; field robotics; Inspection; Kinematics; Locomotion; Machine learning; Robot learning; robotics; Robots; Rusting; Stability analysis; Steel bridges; Steel structures; Steering; wheeled robotics
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22266

This paper introduces an innovative and streamlined design of a robot, resembling a bicycle, created to effectively inspect a wide range of ferromagnetic structures, even those with intricate shapes. The key highlight of this robot lies in its mechanical simplicity coupled with remarkable agility. The locomotion strategy hinges on the arrangement of two magnetic wheels in a configuration akin to a bicycle, augmented by two independent steering actuators. This configuration grants the robot the exceptional ability to move in multiple directions. Moreover, the robot employs a reciprocating mechanism that allows it to alter its shape, thereby surmounting obstacles effortlessly. An inherent trait of the robot is its innate adaptability to uneven and intricate surfaces on steel structures, facilitated by a dynamic joint. To underscore its practicality, the robot's application is demonstrated through the utilization of an ultrasonic sensor for gauging steel thickness, coupled with a pragmatic deployment mechanism. By integrating a defect detection model based on deep learning, the robot showcases its proficiency in automatically identifying and pinpointing areas of rust on steel surfaces. The paper undertakes a thorough analysis, encompassing robot kinematics, adhesive force, potential sliding and turn‐over scenarios, and motor power requirements. These analyses collectively validate the stability and robustness of the proposed design. Notably, the theoretical calculations established in this study serve as a valuable blueprint for developing future robots tailored for climbing steel structures. To enhance its inspection capabilities, the robot is equipped with a camera that employs deep learning algorithms to detect rust visually. The paper substantiates its claims with empirical evidence, sharing results from extensive experiments and real‐world deployments on diverse steel bridges, situated in both Nevada and Georgia. These tests comprehensively affirm the robot's proficiency in adhering to surfaces, navigating challenging terrains, and executing thorough inspections. A comprehensive visual representation of the robot's trials and field deployments is presented in videos accessible at the following links: https://youtu.be/Qdh1oz_oxiQ and https://youtu.be/vFFq79O49dM.