Abstract

This paper presents a novel design of a multi-directional bicycle robot, which is developed for the inspection of steel structures, in particular, steel-reinforced bridges. The locomotion concept is based on arranging two magnetic wheels in a bicycle-like configuration with two independent steering actuators. This configuration allows the robot to possess multi-directional mobility. An additional free joint helps the robot adapt naturally to non-flat and complex steel structures. The robot's design provides the advantage of being mechanically simple and providing high-level mobility across diverse steel structures. In addition, a visual sensor is equipped that allows the data collection for steel defect detection with offline training and validation. The paper also provides a novel pipeline for Steel Defect Detection, which utilizes multiple datasets (one for training and one for validation) from real bridges. The quantitative results have been reported for three Deep Encoder-Decoder Networks (i.e., LinkNet, UNet, DeepLab) with their corresponding Encoder modules (i.e., ResNet-18, ResNet-34, RegNet-X2, EfficientNet-B0, and EfficientNet-B2). Due to space concerns, the qualitative results have been outlined in Appendix, with a link in Fig. 11 caption to access the result provided.

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