Modelling and Analysis of Autonomous Airport Surface Movement Operations based on Multi-Agent Planning

Both EASA and SESAR JU define a vision and roadmap towards an autonomous air traffic management system. Furthermore, past and ongoing SESAR JU projects investigate how to increase the efficiency and predictability of current operations by means of automation. In this paper, we explore the operational implications that result from fully-automated airport surface movement operations modelled with high realism. A hierarchical multi-agent system model was developed to coordinate and control all movements on the airport surface. It comprises the Airport Operations Agent to handle the flight schedule and runway configuration, the Routing Agent to compute conflict-free trajectories, and the Guidance Agents to instruct and monitor the Aircraft Agents while these execute the planned routes. The model incorporates the decisive processes and elements of airport surface movement operations such as pushback, engine-start, inbound and outbound holding, compliance to CTOT-slots, and wake turbulence separation for takeoffs. To compute conflict-free trajectories for all taxiing agents, we tailored and extended state-of-the-art multi-agent motion planning algorithms: the two-level routing algorithm combines Priority-Based Search (PBS) with Safe Interval Path Planning (SIPP). We defined different sizes of aircraft, accounted for a minimal safety distance between them, and calibrated their speed limits in curves with historic ADS-B data. Using the real-world flight schedules of two of the busiest days at Amsterdam Airport Schiphol, including different runway configurations, we examine the performance of the autonomous taxiing system with respect to the historic operations. For the considered simulation conditions, we show that the MAS yields 30% lower taxi times that vary less and are more predictable, and increases runway capacity.