The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve the utilization efficiency of airspace and reduce flight delays and the dependence on ground navigation facilities. The approach stage is one of the most important and difficult stages in the whole flying. In this study, we proposed deep reinforcement learning (DRL)-based RNP procedure execution, DRL-RNP. By conducting an RNP approach procedure, the DRL algorithm was implemented, using a fixed-wing aircraft to explore a path of minimum fuel consumption with reward under windy conditions in compliance with the RNP safety specifications. The experimental results have demonstrated that the six degrees of freedom aircraft controlled by the DRL algorithm can successfully complete the RNP procedure whilst meeting the safety specifications for protection areas and obstruction clearance altitude in the whole procedure. In addition, the potential path with minimum fuel consumption can be explored effectively. Hence, the DRL method can be used not only to implement the RNP procedure with a simulated aircraft but also to help the verification and evaluation of the RNP procedure.

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

Current flight procedure design primarily relies on expert experience, lacking a systematic approach to comprehensively balance safety, route simplification, and environmental impact. To address this challenge, this paper proposes a reinforcement-learning-based method that leverages carefully crafted reward engineering to achieve an optimized flight procedure design, effectively considering safety, route simplicity, and environmental friendliness. To further enhance performance by tackling the low sampling efficiency in the replay buffer, we introduce a multi-objective sampling strategy based on the Pareto frontier, integrated with the soft actor–critic (SAC) algorithm. Experimental results demonstrate that the proposed method generates executable flight procedures in the BlueSky open-source flight simulator, successfully balancing these three conflicting objectives, while achieving a 28.6% increase in convergence speed and a 4% improvement in comprehensive performance across safety, route simplification, and environmental impact compared to the baseline algorithm. This study offers an efficient and validated solution for the intelligent design of flight procedures.

Flight procedures are essential to the safety and efficiency of air traffic management. However, due to the highly specialized nature of the flight procedure design process, existing methods rely heavily on manual operations and adjustments with limited automation, resulting in inefficiencies and potential safety risks. This study introduces AutoFPDesigner, a new agent-driven approach to flight procedure design, leveraging large language models (LLMs). By utilizing multi-agent collaboration, AutoFPDesigner automates Performance-Based Navigation (PBN) procedures, enabling end-to-end automation. In this framework, the designer’s role shifts from an executor to a supervisor, issuing tasks through natural language, while the system integrates specialized knowledge and uses a toolset to complete the design. Experimental results show that procedures designed with this approach meet safety requirements nearly 100%, with 75% of tasks completed in a limited number of steps. Moreover, AutoFPDesigner performs effectively across various design tasks, outperforming existing methods. Additionally, this study conducted human interaction experiments and introduced an “instruction-based” feedback method to address agent misinterpretation of human feedback. Experimental results demonstrate that the system bridges the skill gap between experts and beginners, and that the “instruction-based” feedback method enhances the accuracy of agent feedback interpretation. Code and data are available on https://github.com/Zhulongtao6/AutoFPDesigner-LLM