Abstract
The introduction of autonomous mobile robotic systems into ground handling operations at airports has been limited by aviation safety requirements and the high costs associated with a robot colliding with an aircraft. To ensure safety, traditional robotic navigation methods use static buffer zones, which limit the functionality of robotic systems working near the aircraft fuselage. A probabilistic risk assessment model was developed in this study to simulate close-in operation of heterogeneous mobile robotic systems around an aircraft. The proposed model employs a hybrid framework that integrates an extended Kalman filter, Monte Carlo simulations, and a Bayesian network to consider the kinematic uncertainty of a robot, random environmental conditions, and sensor data for real-time evaluation of collision probabilities. The modeling of near-aircraft inspection scenarios conducted in MATLAB demonstrated the feasibility of the proposed approach: the system successfully completed 49 out of 50 simulated missions while testing landing gear inspection scenarios. In addition, the modeling reduced the minimum distance to the inspection object to 0.48 m, compared with a baseline safe distance of 2 m. These results are interpreted as a simulation-based verification of the feasibility of the proposed approach rather than as an operational validation. Experiments, including hardware modeling, data from real sensors, and controlled tests on the airport apron, are required before implementing the approach in real-world conditions.