Abstract
Abstract Bridge bearings are a critical component of a bridge and require regular visual inspection to ensure the safe operation of the bridge throughout its life. However, the bearings are often located in spaces that are difficult or hazardous to reach, which can impact how often the bearings are inspected. In addition, these spaces are small and offer significant challenges for tele-operation due to line-of-sight restrictions; hence, some level of autonomy is required to make robotic inspection possible. In this work, a robotic solution to bridge bearing inspection is presented, and localisation methods are assessed as the first, and most, important step towards automation. Robot localisation is performed in both a lab environment and a real bridge bearing environment. In this paper, Adaptive Monte-Carlo Localisation is considered for localisation in a known map and gave comparable results to Hector-SLAM, with all results less than a defined error threshold of 10 cm. A combination of both of these methods are proposed to give a more robust approach that gives errors lower than the defined threshold in the real bridge. The experiments also show the need to provide an accurate starting point for each inspection within the bearing, for which we notionally suggest the use of a docking station that could also be used for improved autonomy, such as charging. In addition, proof-of-concept approaches for visual inspection tasks, such as geometry changes and foreign object detection are presented to show some of the proposed benefits of the system presented in this work.
Highlights
Bridge bearings transfer the loads from the superstructure of bridges to the abutments or intermediate supports, which transfer these loads to the bridge foundations
This paper focuses on the problem of localisation and mapping since it is critical for any further development of autonomous technology for bridge bearing inspection
The trajectories resulting from Adaptive Monte-Carlo Localisation (AMCL) when localising using the map created by Hector Simultaneous Localisation and Mapping (SLAM) are plotted against the trajectories from AMCL when localising in the map created from a Structure from Motion (SfM) point cloud, and both trajectories are compared to the ground-truth trajectory of the robot and to the trajectory calculated by Hector SLAM
Summary
Bridge bearings transfer the loads from the superstructure of bridges (e.g., the deck) to the abutments or intermediate supports, which transfer these loads to the bridge foundations. Bearings are an integral part of bridge structures and their failure can have considerable impact on the bridge life [1, 2], leading to the overall failure of the entire bridge [3]. It is not uncommon for bridge bearings to be replaced at high costs and disruption (e.g., [4]). The inspection requirements for structural bridge bearings are detailed in the relevant European Standard [7] as: “close visual inspection without measurements, spaced at equal, reasonably frequent, intervals ”, with inspections occurring at least as often as the bridge structure is assessed. The standard requires that the bearings are assessed for visible defects including: cracks, incorrect position of the bearing, unforeseen movements and deformations of the bearing and visible defects on the bearing or surrounding structure
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