An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation

  1. Li, Chenglong 1
  2. Gu, Wenyong 1
  3. Zheng, Yuan 2
  4. Huang, Longyang 1
  5. Zhang, Xuejun 3
  6. Tavares Calafate, Carlos
  7. González Aguilera, Diego 4
  1. 1 College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China
  2. 2 School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
  3. 3 School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
  4. 4 Universidad de Salamanca

    Universidad de Salamanca

    Salamanca, España



ISSN: 2504-446X

Year of publication: 2023

Volume: 7

Issue: 5

Pages: 334

Type: Article

DOI: 10.3390/DRONES7050334 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Drones


Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.

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