A Deep Reinforcement Learning Path Planning Method for Urban Heavy-Lift eVTOL Considering Motion Attitude Constraints

Heavy-lift electric vertical take-off and landing aircraft (eVTOL) play a vital role in advancing the low-altitude economy by enabling the transportation of high payloads in dense urban environments. However, autonomous navigation in such environments presents significant challenges for heavy-lift eVTOLs due to the large moments of inertia and limited actuator capabilities, which hinder agile maneuvers and effective obstacle avoidance in confined spaces. Motivated by the mentioned issue, a deep reinforcement learning (DRL) path planning method that explicitly incorporates a data-driven dynamic flight characteristic model (DFCM) is introduced to plan safe and feasible flight paths for heavy-lift eVTOLs. Firstly, the DFCM is formulated to fully consider the flight control performance of the heavy-lift eVTOL. And then, utilizing the built DFCM, the real-time feasible attitude domain can be obtained to constrain the autonomous motion attitude of the heavy-lift eVTOL. Thirdly, the path planning method considering motion attitude constraints is established through the DRL process. The accuracy of the proposed model has been verified using real flight data, with the corresponding correlation coefficients amounting to 0.9723. The experimental planning results show that the proposed method generate safe and executable trajectories within the feasible attitude domain.