DDQNC-P: A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions

Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning, which requires superior real-time performance and conflict-free reliability of solving methods. Multi-aircraft real-time 4D trajectory planning under adverse weather is an essential problem in Air Traffic Control (ATC) and it is challenging for the existing methods to be applied effectively. A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing (DDQNC-P) is proposed to solve this problem. An Agent for two aircraft synergetic trajectory planning is trained by the Deep Reinforcement Learning (DRL) model of DDQNC, which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions. Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning, making the multi-aircraft trajectory planning problem processable for the trained Agent. This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly. Substantial simulations with various aircraft numbers, weather conditions, and airspace structures were conducted for performance verification and comparison. The success rate of conflict-free trajectory resolution reached 96.56% with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft, finally confirming its applicability to make real-time decision-making support for controllers in real-world ATC systems.