Dynamic collision avoidance using local cooperative airplanes decisions

Abstract

In the near future, air traffic control (ATC) will have to cope with a radical change in the structure of air transport [1]. Apart from the increase in traffic that will push the system to its limits, the insertion of new aerial vehicles such as drones into the airspace, with different flight performances, will increase its heterogeneity. Current research aims at increasing the level of automation and partial delegation of the control to on-board systems. In this work, we investigate the collision avoidance management problem using a decentralized distributed approach. We propose an autonomous and generic multiagent system to address this complex problem. We validate our system using state-of-the-art benchmarks. The results underline the adequacy of our local and cooperative approaches to efficiently solve the studied problem.

Management of Intelligent Vehicles: Comparison and Analysis

“…The presence of non CAV could be addressed by considering them as non cooperative entities with unpredictable behaviour. The CAV must be able to identify them and learn how to interact with them, with avoidance strategy (Degas et al, 2019) and the exchange of information without communication.…”

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

“…A majority of these works focus on optimising the traffic and/or avoiding collisions, from the point of view of the trajectories. 5D Traffic Optimisation works focus in general on one flight phase, such as optimising (a) En-Route traffic, using centralised, i.e., SA [46] or EA [47], or decentralised, i.e., MAS [48,49]; (b) arrival traffic [50]; (c) departure traffic [51]; (d) Ground Traffic ("5D" Traffic) Optimisation-although this traffic is not really using altitude, it is still noted 5D Traffic in the categorisation to avoid confusion between traffic and trajectory-; or (e) the whole CTR Traffic [52]. Notable other focus of AI model for optimisation are optimising Airspace Structure, such as Route network [53], Sectors [54], and Optimising a 4D Trajectory [55], optimising the Demand/Capacity Balance, or the Route of an Aircraft.…”