  Autonomous navigation of steel bridge inspection robots is essential for
proper maintenance. The majority of existing robotic solutions for steel bridge
inspection requires human intervention to assist in the control and navigation.
In this paper, 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 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.
