Abstract:
In order to lower the ramp risk and improve the aircraft ground handling efficiency, we aim to: (a) Track ground support equipment (GSE) in a real-time and high-accuracy manner so that we can not only conveniently obtain the positions and velocities of them but also reliably report latent collisions among aircraft and GSE. As a result, corresponding ramp risks could be detected and handled in advance; (b) Schedule the GSE in an optimal manner based on the real-time data gathered in advance to make efficient use of GSE so that we can smoothly serve the annually increasing air traffic while controlling the ramp area congestion and GSE overheads
This project express to provide a solution for such a problem we are going to proposed an (Artificial Intelligence) AI system which instantly notifies the Ground Support Equipment while moving the airport whenever an GSE in airport could make accident takes place and pinpoints its probability to take place. When an accident could take place, an ultrasonic sensor is used to get the surrounding GSE distances and camera sensor will be used to identify and recognize the surrounding GSE. Then, an algorithm is applied to process the sensor signal and alarm the driver using buzzer and change the text o GSE vehicle recognized will be speeches for the GSE driver. The GSE training for 17 equipment (aero plane, stairs, catering, services, tugs, medical lift, rescue, gpu, cargo loader, belt loader, starter unit, tractor, towing bar, conditioning unit, dolly, apron, and refueler) is achieved. The results for the project is achieved using video and camera live stream for detecting the GSE vehicles with average 89% accuracy and 39 milliseconds detection time
