Design and Experimental Evaluation of an Aerial Solution for Visual Inspection of Tunnel-like Infrastructures

Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. Contributing to this aim, the present work describes an aerial robotic solution designed to perform autonomous inspections of tunnel-like infrastructures. The proposed robotic system is equipped with visual and thermal sensors and uses an inspection-driven path planning algorithm to generate a path that maximizes the quality of the gathered data in terms of photogrammetry goals while optimizing the surface coverage and the total trajectory length. The performance of the planning algorithm is demonstrated in simulation against state-of-the-art methods and a wall-following inspection trajectory. Results of a real inspection test conducted in a railway tunnel are also presented, validating the whole system operation.

Automated summary

Current railway tunnel inspections rely on expert operators performing visual examinations and manually annotating defects. This study proposes an aerial robotic solution equipped with visual and thermal sensors to autonomously inspect tunnel-like infrastructures, optimizing data quality and surface coverage through a path planning algorithm.