Autonomous navigation is essential for steel bridge inspection robot to monitor and maintain the working condition of steel bridge. Majority of existing robotic solutions requires human support to navigate the robot doing the inspection. In this paper, a navigation framework is proposed for ARA robot [1], [2] to run on mobile mode. In this mode, the robot needs to cross and inspect all the available steel bars. The most significant contributions of this research are four algorithms, which can process the depth data, segment it into clusters, estimate the boundaries, construct a graph to represent the structure, generate a 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. We released our source code in Github for the research community to use.

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 allow 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.