Cooperative Urban Air Mobility Trajectory Design for Power and AoI Optimization: A Multi-Agent Reinforcement Learning Approach

Urban air mobility (UAM) is a revolutionary urban transportation paradigm that aims to transport passengers, emphasizing safety, power efficiency, and autonomous operation. Also, UAM aircraft needs to regularly transmit status and environmental conditions to the base stations (BS) to maintain updated information in the system. Hence, the age of information (AoI) can be used as a status measurement indicator to evaluate the degree to which the current status information of UAM aircraft is outdated. Optimizing each UAM aircraft's flight trajectory for both power efficiency and safety, while also minimizing the AoI for timely data transmission forms a computationally challenging NP-hard problem. In this paper, a multi-agent deep reinforcement learning based solution is proposed to address this problem by exploiting a sophisticated method to handle multidimensional decision spaces for power efficiency, safety in trajectory planning, and AoI. Simulation results demonstrate that our method produce a power efficiency improvement of 8.8% compared to the PPO method and 13.8% compare to the detour method. Furthermore, the proposed approach significantly improve the data freshness by up to 31.5%, 31.7%, and 12.9% in terms of AoI compared to the greedy, detour, and single PPO methods, respectively.