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Design of a Human-in-the-Loop Aircraft Taxi Optimisation System Using Autonomous Tow Trucks

机译:基于自动拖车的人在飞机滑行优化系统设计

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One of the solutions proposed by the aerospace industry to reduce fuel consumption, air pollution and noise at an airport consists of using electric tow trucks to tow aircraft from the gate to the runway (or vice-versa). However, the introduction of tow trucks would result in an increase in vehicle traffic at the airport, potentially increasing the workload of Air Traffic Controllers (ATC). This paper proposes an algorithm - based on Dijkstra's algorithm - and a Human Machine Interface (HMI) concept to optimise airport taxi operations with autonomous tow trucks at a strategic level, while keeping ATC in the loop. Preliminary tests of the algorithm have been carried out using simulated traffic data for Malta International Airport (MIA) and the results show that the algorithm can be tuned to minimise the average vehicle taxi delay or the number of unresolved vehicle conflicts. It is also shown that, in certain cases, both the taxi delay and the number of conflicts can be reduced through a minor adjustment of the Off-Block Time (OBT) of departing aircraft.
机译:航空航天工业提出的减少机场燃料消耗,空气污染和噪音的解决方案之一是使用电动拖车将飞机从登机口拖到跑道(反之亦然)。但是,引入拖车会导致机场的车辆流量增加,可能会增加空中交通管制员(ATC)的工作量。本文提出了一种基于Dijkstra算法的算法,以及一种人机界面(HMI)概念,可在战略水平上优化自动无人驾驶卡车的机场滑行操作,同时使ATC处于循环中。已经使用马耳他国际机场(MIA)的模拟交通数据对算法进行了初步测试,结果表明,可以对算法进行调整,以最大程度地减少平均出租车滑行延迟或未解决的车辆冲突的数量。还表明,在某些情况下,滑行延迟和冲突数量可以通过对起飞飞机的离站时间(OBT)进行较小的调整而减少。

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