AI-based Dynamic Re-routing for Dense Low-Altitude Air Traffic Management

Yibing Xie, Alessandro Gardi, Man Liang, Roberto Sabatini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Thanks to their rapid uptake in various industries, an increasing number of Uninhabited Aircraft Systems (UAS) and other emerging aerospace platforms is expected to operate in the shared airspace. Viable conflict detection and resolution as well as demand-capacity balancing (DCB) services will be required to ensure the desired level of safety, particularly with the proliferation of Beyond Line-of-Sight (BLOS) operations. This paper proposes a novel UAS Traffic Management (UTM) system DCB functionality adopting multiple Artificial Intelligence (AI) algorithms to manage both regular and emergency situations. The system is based on a four-dimensional trajectory (4DT) planning algorithm with a flexible DCB process and solution framework. The method is not limited to fixed routing, but can also adjust dynamically to evolving conditions. The selected AI techniques are based on the most suitable machine learning and metaheuristic algorithms. Simulation case studies demonstrate that the proposed method allows to achieve a safe and efficient management of dense traffic in low-altitude airspace around cities and suburbs.

Original languageAmerican English
Title of host publicationDASC 2023 - Digital Avionics Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350333572
StatePublished - 2023
Event42nd IEEE/AIAA Digital Avionics Systems Conference, DASC 2023 - Barcelona, Spain
Duration: 1 Oct 20235 Oct 2023

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209


Conference42nd IEEE/AIAA Digital Avionics Systems Conference, DASC 2023


  • 3D Path Planning
  • 4D Trajectory
  • ATM
  • DCB
  • Machine Learning
  • Metaheuristics Algorithm
  • UTM


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