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.

Guiding an aircraft to 4D waypoints at a certain heading is a multi-dimensional goal aircraft guidance problem. [d=Zu]In order to improve the performance and solve this problem, this paper proposes a multi-layer RL approach.To enhance the performance, in the present study, a multi-layer RL approach to solve the multi-dimensional goal aircraft guidance problem is proposed. The approach [d=Zu]enablesassists the autopilot in an ATC simulator to guide an aircraft to 4D waypoints at certain latitude, longitude, altitude, heading, and arrival time, respectively. To be specific, a multi-layer RL [d=Zu]approach is proposedmethod to simplify the neural network structure and reduce the state dimensions. A shaped reward function that involves the potential function and Dubins path method is applied. [d=Zu]Experimental and simulation results show that the proposed approachExperiments are conducted and the simulation results reveal that the proposed method can significantly improve the convergence efficiency and trajectory performance. [d=Zu]FurthermoreFurther, the results indicate possible application prospects in team aircraft guidance tasks, since the aircraft can directly approach a goal without waiting in a specific pattern, thereby overcoming the problem of current ATC simulators.

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.

Forward-looking sonar (FLS) can capture high-resolution acoustical images from the underwater scenes, maintaining performance even in turbid water conditions and poor lighting. Although neural fields have become popular for 3-D reconstruction from acoustical images, they still suffer from poor accuracy and slow computation. We thus propose a novel framework, namely, neural fields for FLS, which leverages a combination of advanced encoding strategies and tailored loss functions for rapid and accurate object-level 3-D reconstruction. The framework introduces multiresolution hash encoding combined with frequency positional encoding to efficiently represent spatial features, thereby enhancing the capture of scene characteristics. A novel ray sampling strategy is also developed to improve training efficiency while preserving the reconstruction quality. In addition, the proposed framework incorporates a target encoding approach alongside a classification loss to robustly handle noise-rich sonar data, while a structural intensity constraint ensures consistent capture of the intrinsic features of sonar images. Quantitative and qualitative validations using simulated and real scenarios demonstrate substantial improvements in both reconstruction accuracy and training efficiency over existing state-of-the-art methods. The combination of these strategies forms a unified framework that addresses the challenges, providing a more stable, detailed, and computationally efficient solution for FLS-based 3-D reconstruction.

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.