This is an open access article distributed under the CC BY 4.0
Volume 21 article 1048 pages: 36-44
In the examination of critical infrastructure for failure, common problems faced are restricted access to the inspection site, size and geometry constraints, cost, and extended inspection period. Facilities such as marine vessels, petrochemical pressure vessels, rail lines, and airplane fuselage, are regularly inspected. Mostly manual techniques with sensors like cameras and non-destructive testing kits are usually employed in detecting structural defects such as cracks and corrosion which constitute the central part of the cost and time spent. This paper, therefore, describes the design of a modular climbing robot for industrial inspection of structures. The main aim of improving and automating defect classification and identification is achieved by applying computer vision with an embedded wireless camera. YOLOv4 machine learning algorithm is implemented to identify and classify surface cracks and corrosion. The robot design combines a set of 6-DOF modular arm and tracked locomotion system. Embedded magnets are integrated into the design to aid navigation on vertical ferromagnetic structures and uneven surfaces. The final design shows that the robot can successfully navigate ferromagnetic structures, detect defects, and climb over moderately sized obstacles without loss of adhesion. This ensures performance in unfriendly and inaccessible environments, reducing costs and inspection time considerably.
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