Abstract
The continuing development of electric vertical take-off and landing (eVTOL) aircraft presents promising opportunities to alleviate transportation congestion. However, designing and executing takeoff trajectories that optimally balance energy usage, passenger comfort, and aircraft constraints remains challenging. Conventional design optimization provides a solution for pre-defined flight conditions and environments but is not ideal in real-world applications. In contrast, deep reinforcement learning (DRL) implements optimal policy making real-time decisions with no assumption of mathematical models. Seeing the lack of literature on DRL-based takeoff trajectory design of eVTOL aircraft, we implement and conduct DRL-based optimal takeoff trajectory designs for the Airbus3 Vahana drone in this work. Specifically, we aim to achieve optimal takeoff trajectory designs with the minimum energy consumptions while satisfying takeoff metrics (i.e., vertical displacement and horizontal velocity) by varying control profiles (i.e., power and wing angle to the vertical). Results revealed that the trained DRL policy managed to realize the takeoff by meeting all metrics. Moreover, the verification against the simulation-based optimization reference presented that the DRL-based optimal takeoff trajectory achieved 96.3% accuracy on the optimal energy consumption. The DRL-based optimal control profiles also showed similar trends as the optimal references.
Recommended Citation
N. M. Roberts et al., "Deep Reinforcement Learning-Based Optimal Takeoff Trajectory Design of an eVTOL Drone," AIAA Aviation Forum and Ascend 2025, American Institute of Aeronautics and Astronautics, Jan 2025.
The definitive version is available at https://doi.org/10.2514/6.2025-3800
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Full Access
Keywords and Phrases
Airbus; Drone; Electric Vertical Take off and Landing; Energy Consumption; Lift Coefficient; Mathematical Models; Multidisciplinary Design and Optimization; Reinforcement Learning; Trajectory Design; Trajectory Optimization
International Standard Book Number (ISBN)
978-162410738-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Jan 2025
