Adhesion Risk Assessment of An Aircraft Inspection Robot for Improving Operator Awareness

Abstract

Vacuum-adhesion-based climbing robots have been developed to cater to the demands in the cleaning and inspection work of airplanes. A robot intended to clean and inspect an airplane faces a Risk of Adhesion (RoA) based on the robot and the surface conditions, such as worn-out suction cups. These sorts of underlying conditions are not easily noticeable for an operator of a robot and might lead to catastrophic events. Therefore, the ability of a robot to self-assess the RoA in a scenario and notify the operator is crucial for ensuring safety. Particularly, an aircraft inspection robot should have adhesion awareness. This paper proposes a novel method to self-assess the RoA of a vacuum-adhesion-based robot intended to clean and inspect airplanes. The RoA is assessed by a fuzzy inference system that analyzes the present pressure difference and the current duty setting of the vacuum pump of a robot. The robot operator can collaborate with the robot to take precautions based on the assessed RoA to ensure safety. The outcomes of the experiment conducted on an airplane skin validate the ability of the proposed method to assess the RoA associated with heterogeneous operating conditions effectively. Thus, the utilization of the proposed method would improve the safety of a vacuum-adhesion-based robot intended to clean and inspect airplanes.