TY - JOUR
T1 - A Deep Reinforcement Learning Path Planning Method for Urban Heavy-Lift eVTOL Considering Motion Attitude Constraints
AU - Liu, Wenjie
AU - Yang, Chao
AU - Wang, Weida
AU - Qie, Tianqi
AU - Chen, Yincong
AU - Yong, Shuanghao
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2026
Y1 - 2026
N2 - Heavy-lift electric vertical take-off and landing (eVTOL) aircraft 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. First, 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. Third, 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.
AB - Heavy-lift electric vertical take-off and landing (eVTOL) aircraft 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. First, 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. Third, 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.
KW - Autonomous flight
KW - deep reinforcement learning (DRL)
KW - electrical vertical take-off and landing (eVTOL)
KW - path planning
KW - urban air mobility
UR - https://www.scopus.com/pages/publications/105019564435
U2 - 10.1109/TTE.2025.3621634
DO - 10.1109/TTE.2025.3621634
M3 - Article
AN - SCOPUS:105019564435
SN - 2332-7782
VL - 12
SP - 825
EP - 837
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
IS - 1
ER -