skip to main content

Title: Control Framework for a Hybrid-steel Bridge Inspection Robot
Autonomous navigation of steel bridge inspection robots are essential for proper maintenance. Majority of existing robotic solutions for bridge inspection require human intervention to assist in the control and navigation. In this paper, a control system framework has been proposed for a previously designed ARA robot [1], which facilitates autonomous real-time navigation and minimizes human involvement. The mechanical design and control framework of ARA robot enables two different configurations, namely the mobile and inch-worm transformation. In addition, a switching control was developed with 3D point clouds of steel surfaces as the input which allows the robot to switch between mobile and inch-worm transformation. The surface availability algorithm (considers plane, area and height) of the switching control enables the robot to perform inch-worm jumps autonomously. The mobile transformation allows the robot to move on continuous steel surfaces and perform visual inspection of steel bridge structures. Practical experiments on actual steel bridge structures highlight the effective performance of ARA robot with the proposed control framework for autonomous navigation during visual inspection of steel bridges.
; ; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page Range or eLocation-ID:
2585 to 2591
Sponsoring Org:
National Science Foundation
More Like this
  1. Motivated by a high demand for automated inspection of civil infrastructure, this work presents an efficient design and development of a tank-like robot for structural health monitoring. Unlike most existing magnetic wheeled mobile robot designs, which may be suitable for climbing on flat steel surface, our proposed tank-like robot design uses reciprocating mechanism and roller-chains to make it capable of climbing on different structural shapes (e.g., cylinder, cube) with coated or non-coated steel surfaces. The developed robot is able to pass through the joints and transition from one surface to the other (e.g., from flat to curving surfaces). Taking into account several strict considerations (including tight dimension, efficient adhesion and climbing flexibility) to adapt with various shapes of steel structures, a prototype tank-like robot integrating multiple sensors (hall-effects, sonars, inertial measurement unit, Eddy current and cameras), has been developed. Rigorous analysis of robot kinematics, adhesion force, sliding failure and turn-over failure has been conducted to demonstrate the stability of the proposed design. Mechanical and magnetic force analysis together with sliding/turn-over failure investigation can serve as an useful framework for designing various steel climbing robots in the future. The robot is integrated with cameras and Eddy current sensor for visual andmore »in-depth fatigue crack inspection of steel structures. Experimental results and field deployments confirm the adhesion, climbing, inspection capability of the developed robot.« less
  2. The research of robots to assist people in inspecting the quality of steel bridges has attracted significant attention in recent years. However, the intricate structure of the steel bridge components poses a massive challenge for researchers to move the robot across the bridge to perform the tests. This paper presents a new development of a hybrid flying-climbing robotic system, which can move flexibly and quickly to different positions on the steel bridge. In addition to using high-resolution cameras for an overview, the design allows the robot to stick to steel surfaces and act as a mobile robot for more detailed inspection with our developed giant magneto-resistance (GMR) sensor array system. We conduct a mechanical analysis to show the climbing capability of the mobile part. Additionally, we develop a landing algorithm to allow the robot to land on a steel surface to perform in-depth inspection safely. The designed GMR sensor array has shown the capability of detecting steel cracks to support the in-depth inspection mode. We have tested and validated our developed robot on real bridges to ensure that the design works well and is stable.
  3. The Advanced Robotics and Automation (ARA) Lab has engineered its next-generation robot for steel bridge inspection. This particular design is specialized for its particularly high strength adhesion force and high maneuverability. The robot can utilize various steering configurations such as Ackermann, synchronous and static point steering while navigating steel structures and adhering to cylindrical members. The adhesion system creates a comprehensive platform for adding extra sensing equipment by the user and will serve as a basis for future works. This paper will discuss in detail the design work done to ensure that the proposed robot would function as intended before we made it and show how the capabilities we engineered the proposed robot have made it a step forward for the steel inspection industry.
  4. null (Ed.)
    Abstract This work proposes vision-only navigation strategies for an autonomous underwater robot. This approach is a step towards solving the coverage path planning problem in a 3-D environment for surveying underwater structures. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work, we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this article is a step towards enabling the autonomous operation of lightweight robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3-D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation with 80% accuracy onmore »the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed.« less
  5. The advanced robotic and automation (ARA) lab has developed and successfully implemented a design inspired by many of the various cutting edge steel inspection robots to date. The combination of these robots concepts into a unified design came with its own set of challenges since the parameters for these features sometimes conflicted. An extensive amount of design and analysis work was performed by the ARA lab in order to find a carefully tuned balance between the implemented features on the ARA robot and general functionality. Having successfully managed to implement this conglomerate of features represents a breakthrough to the industry of steel inspection robots as the ARA lab robot is capable of traversing most complex geometries found on steel structures while still maintaining its ability to efficiently travel along these structures; a feat yet to be done until now.