Climbing Robot for Steel Bridge Inspection: Design Challenges

Inspection of bridges often requires high risk operations such as working at heights, in confined spaces, in hazardous environments; or sites inaccessible by humans. There is significant motivation for robotic solutions which can carry out these inspection tasks. When inspection robots are deployed in real world inspection scenarios, it is inevitable that unforeseen challenges will be encountered. Since 2011, the New South Wales Roads & Maritime Services and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney, have been working together to develop an innovative climbing robot to inspect high risk locations on the Sydney Harbour Bridge. Many engineering challenges have been faced throughout the development of several prototype climbing robots, and through field trials in the archways of the Sydney Harbour Bridge. This paper will highlight some of the key challenges faced in designing a climbing robot for inspection, and then present an inchworm inspired robot which addresses many of these challenges

Doctor of Philosophy

dissertationThis dissertation defines a new class of climbing robots, steering-plane bipeds, which encompasses a large number of existing climbing robots. Three major levels of motion planning are characterized which are common to this class of robots, namely, path planning, step planning, and gait planning. The unified presentation of related motion planning techniques is more generally applicable and more thorough than related algorithms in other literature, while more explicitly identifying limitations and tradeoffs due to alternate design choices within the class of steering-plane bipeds. A novel spline-based method for generating gaits is presented which uses separate path and time rate controls, and explicitly defined foot approach and departure directions that allows 1) a nominal guarantee of collision-free foot trajectories when close to the desired step configuration, 2) independent control of gait shape and speed, and 3) a unified representation of the four gait families of steering-plane bipeds: flipping, inchworm, step-through, and spinning gaits. This dissertation presents a thorough examination of the variations within each gait family, rather than merely presenting a representative instance of each. Concrete case studies applying the techniques of this dissertation are presented for optimizing the gaits for overall speed, energy efficiency, and minimum gripping force and moment. The results highlight that many common gaits in the literature are far from optimal. Results and general rules of thumb for gait planning are extracted that allow guidance for obtaining good results even if using alternate planning techniques without optimization

An approach for real-time motion planning of an inchworm robot in complex steel bridge environments

© Cambridge University Press 2016. Path planning can be difficult and time consuming for inchworm robots especially when operating in complex 3D environments such as steel bridges. Confined areas may prevent a robot from extensively searching the environment by limiting its mobility. An approach for real-time path planning is presented. This approach first uses the concept of line-of-sight (LoS) to find waypoints from the start pose to the end node. It then plans smooth, collision-free motion for a robot to move between waypoints using a 3D-F2 algorithm. Extensive simulations and experiments are conducted in 2D and 3D scenarios to verify the approach

Control and Navigation Framework for a Hybrid Steel Bridge Inspection Robot

Autonomous navigation of steel bridge inspection robots is essential for proper maintenance. Majority of existing robotic solutions for steel bridge inspection require human intervention to assist in the control and navigation. In this thesis, a control and navigation framework has been proposed for the steel bridge inspection robot developed by the Advanced Robotics and Automation (ARA) to facilitate autonomous real-time navigation and minimize human intervention. The ARA robot is designed to work in two modes: mobile and inch-worm. The robot uses mobile mode when moving on a plane surface and inch-worm mode when jumping from one surface to the other. To allow the ARA robot to switch between mobile and inch-worm modes, a switching controller is developed with 3D point cloud data based. The surface detection algorithm is proposed to allow the robot to check the availability of steel surfaces (plane, area and height) to determine the transformation from mobile mode to inch-worm one. To have the robot to safely navigate and visit all steel members of the bridge, four algorithms are developed to process the data from a depth camera, segment it into clusters, estimate the boundaries, construct a graph representing the structure, generate the shortest inspection path with any starting and ending points, and determine available robot configuration for path planning. Experiments on steel bridge structures setup highlight the effective performance of the algorithms, and the potential to apply to the ARA robot to run on real bridge structures