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.

中文翻译：

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钢结构的敏捷机器人检测：具有多传感器集成的类似自行车的方法

本文介绍了一种类似于自行车的创新和流线型机器人设计，旨在有效地检查各种铁磁结构，甚至是形状复杂的铁磁结构。该机器人的主要亮点在于其机械简单性和卓越的灵活性。运动策略取决于两个磁轮的排列，其配置类似于自行车，并由两个独立的转向执行器增强。这种配置赋予机器人在多个方向移动的卓越能力。此外，该机器人采用往复运动机构，可以改变其形状，从而毫不费力地越过障碍。该机器人的一个固有特征是其对钢结构上不平坦和复杂表面的天生适应性，这得益于动态关节。为了强调其实用性，该机器人的应用通过利用超声波传感器测量钢材厚度以及实用的部署机制来演示。通过集成基于深度学习的缺陷检测模型，该机器人展示了其自动识别和精确定位钢铁表面锈迹区域的能力。该论文进行了全面的分析，包括机器人运动学、附着力、潜在的滑动和翻转场景以及电机功率要求。这些分析共同验证了所提出设计的稳定性和鲁棒性。值得注意的是，这项研究中建立的理论计算为开发未来用于攀爬钢结构的机器人提供了宝贵的蓝图。为了增强其检查能力，机器人配备了摄像头，采用深度学习算法来视觉检测锈迹。该论文用经验证据证实了其主张，分享了在内华达州和佐治亚州的各种钢桥上进行的广泛实验和实际部署的结果。这些测试全面证实了机器人在粘附表面、穿越具有挑战性的地形以及执行彻底检查方面的熟练程度。机器人试验和现场部署的全面可视化展示在视频中，可通过以下链接访问：https://youtu.be/Qdh1oz_oxiQ 和 https://youtu.be/vFFq79O49dM。