Advanced Air Mobility (AAM) has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation. Its core objective is to provide highly automated air transportation services for passengers or cargo, operating at low altitudes within urban, suburban, and rural regions. AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted. In a complex aviation environment, traffic management and control are essential technologies for safe and effective AAM operations. One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections. The escalating demand for air mobility services, particularly within urban areas, poses significant complexities to the execution of such missions. In this study, we propose a novel multi-agent reinforcement learning (MARL) approach to efficiently manage high-density AAM operations in structured airspace. Our approach provides effective guidance to AAM vehicles, ensuring conflict avoidance, mitigating traffic congestion, reducing travel time, and maintaining safe separation. Specifically, intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle, ensuring secure merging into air corridors and safe passage through intersections. To validate the effectiveness of our proposed model, we conduct training and evaluation using BlueSky, an open-source air traffic control simulation environment. Through the simulation of thousands of aircraft and the integration of real-world data, our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations. The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.

Traditionally, numerical trajectory integration for shooting equation calculation and iterations for shooting with randomly guessed initial solutions deteriorate the real-time performance of indirect methods for on-board applications. In this study, the indirect method is improved to achieve real-time trajectory optimization of fuel-optimal powered planetary landings with the help of analytical shooting equation derivations and a practical homotopy technique. Specifically, the contributions of this paper are threefold. First, the analytical expressions for shooting equation calculation are derived to replace the traditional time-consuming trajectory integration. Consequently, the computational efficiency is significantly improved. Second, the original three-dimensional landing problem is connected with a simplified one-dimensional problem that only involves the vertical dynamics, and its analytical solution is obtained based on Pontryagin’s minimum principle. Third, starting with the analytical solution, the accurate solution of the original landing problem can be obtained through an adaptive homotopy process. Simulation results of Earth landing scenarios are given to substantiate the effectiveness of the proposed techniques and illustrate that the developed method can obtain a fuel-optimal landing trajectory in 5 ms with 100% success rate.