Detection and Control Framework for Unpiloted Ground Support Equipment within the Aircraft Stand

The rapid advancement in Unpiloted Robotic Vehicle technology has significantly influenced ground support operations at airports, marking a critical shift towards future development. This study presents a novel Unpiloted Ground Support Equipment (GSE) detection and control framework, comprising virt...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-12, Vol.24 (1), p.205
Main Authors: Zhang, Tianxiong, Zhang, Zhiqiang, Zhu, Xinping
Format: Article
Language:English
Subjects:
Aircraft
Airports
Algorithms
Automation
autonomous driving
Civil aviation
computer vision
Control algorithms
Driverless cars
Human error
multi-sensor fusion
Optical radar
Remote sensing
Sensors
Unpiloted Ground Support Equipment
Vehicles
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Summary:The rapid advancement in Unpiloted Robotic Vehicle technology has significantly influenced ground support operations at airports, marking a critical shift towards future development. This study presents a novel Unpiloted Ground Support Equipment (GSE) detection and control framework, comprising virtual channel delineation, boundary line detection, object detection, and navigation and docking control, to facilitate automated aircraft docking within the aircraft stand. Firstly, we developed a bespoke virtual channel layout for Unpiloted GSE, aligning with operational regulations and accommodating a wide spectrum of aircraft types. This layout employs turning induction markers to define essential navigation points, thereby streamlining GSE movement. Secondly, we integrated cameras and Lidar sensors to enable rapid and precise pose adjustments during docking. The introduction of a boundary line detection system, along with an optimized, lightweight YOLO algorithm, ensures swift and accurate identification of boundaries, obstacles, and docking sites. Finally, we formulated a unique control algorithm for effective obstacle avoidance and docking in varied apron conditions, guaranteeing meticulous management of vehicle pose and speed. Our experimental findings reveal an 89% detection accuracy for the virtual channel boundary line, a 95% accuracy for guiding markers, and an F1-Score of 0.845 for the YOLO object detection algorithm. The GSE achieved an average docking error of less than 3 cm and an angular deviation under 5 degrees, corroborating the efficacy and advanced nature of our proposed approach in Unpiloted GSE detection and aircraft docking.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24010205