Risk-aware autonomous avoidance for eVTOL

Kang ZHANG; Xinmin TANG; Junwei GU

doi:10.7527/S1000-6893.2025.32083

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2026 , Vol. 47 >Issue 2: 332083 - 332083

DOI: https://doi.org/10.7527/S1000-6893.2025.32083

Electronics and Electrical Engineering and Control

Risk-aware autonomous avoidance for eVTOL

Kang ZHANG ,
Xinmin TANG ,
Junwei GU

Expand

^1.College of Civil Aviation，Nanjing University of Aeronautics and Astronautics，Nanjing 211106，China
^2.Key Laboratory of Urban Air Traffic System Technology and Equipment，Civil Aviation University of China，Tianjin 300300，China

E-mail：tangxinmin@nuaa.edu.cn

Received date: 2025-04-07

Revised date: 2025-05-19

Accepted date: 2025-07-01

Online published: 2025-07-15

Supported by

National Natural Science Foundation of China(52072174);High-Level Foreign Experts Introduction Program of China(G2023202003L);Tianjin Municipal Science and Technology Program(24JCZDJC00090)

Fold

Abstract

In the context of urban air traffic， there is currently no mature solution for real-time avoidance of electric Vertical Take-Off and Landing （eVTOL） aircraft in dynamic and uncertain environments. To address this challenge， we propose a risk-aware and efficient motion planning method for real-time eVTOL avoidance. The problem is formulated as a Model Predictive Control （MPC） framework with Chance Constraints MPC （CC-MPC）， incorporating both collision avoidance and geo-fencing constraints. To efficiently handle the chance constraints， we reformulate them using Big-M and confidence ellipsoids， transforming the CC-MPC problem into a Mixed-Integer Programming （MIP） problem. To efficiently solve the MIP， we employ an iterative convexification-based optimization method， complemented by a global search algorithm that serves as a front-end warm-start mechanism. Finally， all components are integrated within a receding horizon control framework to enable fast and dynamic trajectory generation for eVTOLs. Simulation experiments across various flight scenarios demonstrate the effectiveness of the proposed approach.

Key words： urban air traffic; eVTOL; collision avoidance; chance constraints; motion planning

Cite this article

Kang ZHANG , Xinmin TANG , Junwei GU . Risk-aware autonomous avoidance for eVTOL[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(2) : 332083 -332083 . DOI: 10.7527/S1000-6893.2025.32083

References

[1]	THIPPHAVONG D P， APAZA R， BARMORE B， et al. Urban air mobility airspace integration concepts and considerations： AIAA-2018-3676［R］. Reston： AIAA， 2018.
[2]	BARRETT-GONZALEZ R M， DENNELER M， SCHWAB Z， et al . Designing eVTOL and UAM aircraft for flight safety， EMP， and HIRF resistance EMI， certification FAA， insurability， ground safety and community acceptance［C］∥AIAA Aviation 2023 Forum. Reston： AIAA， 2023.
[3]	余莎莎，陈星雨，西华大学. 城市空中交通领域关键技术创新与挑战［J］. 航空学报， 2024， 45（）： 730657.
	YU S S， CHEN X Y， XI H. Key technological innovations and challenges in urban air mobility［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（S1）： 730657.
[4]	邓景辉. 电动垂直起降飞行器的技术现状与发展［J］. 航空学报， 2024， 45（5）： 529937.
	DENG J H. Technical status and development of electric vertical take-off and landing aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（5）： 529937 （in Chinese）.
[5]	吕洋，康童娜，潘泉，等. 无人机感知与规避：概念、技术与系统［J］. 中国科学：信息科学， 2019， 49（5）： 520-537.
	Lü/LV/LU/LYU） Y， KANG T N， PAN Q， et al. UAV sense and avoidance： Concepts， technologies， and systems［J］. Scientia Sinica （Informationis）， 2019， 49（5）： 520-537 （in Chinese）.
[6]	汤新民，顾俊伟，刘冰，等. 低空监视技术及其发展趋势综述［J］. 南京航空航天大学学报， 2024， 56（6）： 973-993.
	TANG X M， GU J W， LIU B， et al. Review on low-altitude surveillance technology and its development trend［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2024， 56（6）： 973-993 （in Chinese）.
[7]	景晓年，梁晓龙，张佳强，等. 无人机感知避让技术分析［J］. 火力与指挥控制， 2017， 42（4）： 1-5.
	JING X N， LIANG X L， ZHANG J Q， et al. Analysis of UAV sense and avoid technology［J］. Fire Control & Command Control， 2017， 42（4）： 1-5 （in Chinese）.
[8]	王兴隆，王友杰. 面向城市低空的多机型eVTOL安全间隔评估［J］. 航空学报， 2025， 46（1）： 330604.
	WANG X L， WANG Y J. Safety interval evaluation for multi-aircraft eVTOL in urban low altitude［J］. Acta Aeronautica et Astronautica Sinica， 2025， 46（1）： 330604 （in Chinese）.
[9]	ZOU Y Y， ZHANG H H， ZHONG G， et al. Collision probability estimation for small unmanned aircraft systems［J］. Reliability Engineering & System Safety， 2021， 213： 107619.
[10]	薛震，盛汉霖，陈欣，等. 基于剪枝可视性地图的无人机全局规划方法［J］. 航空学报， 2025， 46（10）： 331279.
	XUE Z， SHENG H L， CHEN X， et al. Global planning method for UAVs based on pruned visibility map［J］. Acta Aeronautica et Astronautica Sinica， 2025， 46（10）： 331279 （in Chinese）.
[11]	郭华，郭小和. 改进速度障碍法的无人机局部路径规划算法［J］. 航空学报， 2023， 44（11）： 327586.
	GUO H， GUO X H. Local path planning algorithm for UAV based on improved velocity obstacle method［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（11）： 327586 （in Chinese）.
[12]	HART P E， NILSSON N J， RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths［J］. IEEE Transactions on Systems Science and Cybernetics， 1968， 4（2）： 100-107.
[13]	KARAMAN S， WALTER M R， PEREZ A， et al. Anytime motion planning using the RRT［C］∥2011 IEEE International Conference on Robotics and Automation. Piscataway： IEEE Press， 2011： 1478-1483.
[14]	BETTS J T， CAMPBELL S， DIGIROLAMO C. Examination of solving optimal control problems with delays using GPOPS-Ⅱ［J］. Numerical Algebra， Control & Optimization， 2021， 11（2）： 283.
[15]	OSBORNE M R. On shooting methods for boundary value problems［J］. Journal of Mathematical Analysis and Applications， 1969， 27（2）： 417-433.
[16]	WANG Z P， ZHOU X， XU C， et al. Geometrically constrained trajectory optimization for multicopters［J］. IEEE Transactions on Robotics， 2022， 38（5）： 3259-3278.
[17]	JANSON L， SCHMERLING E， PAVONE M. Monte Carlo motion planning for robot trajectory optimization under uncertainty［M］∥Robotics Research. Cham： Springer International Publishing， 2017： 343-361.
[18]	CALAFIORE G， CAMPI M C. Uncertain convex programs： Randomized solutions and confidence levels［J］. Mathematical Programming， 2005， 102（1）： 25-46.
[19]	BLACKMORE L， ONO M， BEKTASSOV A， et al. A probabilistic particle-control approximation of chance-constrained stochastic predictive control［J］. IEEE Transactions on Robotics， 2010， 26（3）： 502-517.
[20]	WU P C， YANG X X， WEI P， et al. Safety assured online guidance with airborne separation for urban air mobility operations in uncertain environments［J］. IEEE Transactions on Intelligent Transportation Systems， 2022， 23（10）： 19413-19427.
[21]	LUDERS B， KOTHARI M， HOW J. Chance constrained RRT for probabilistic robustness to environmental uncertainty［C］∥AIAA Guidance， Navigation， and Control Conference. Reston： AIAA， 2010.
[22]	SAFAOUI S， GRAVELL B J， RENGANATHAN V， et al. Risk-averse RRT* planning with nonlinear steering and tracking controllers for nonlinear robotic systems under uncertainty［C］∥2021 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. Piscataway： IEEE Press， 2021： 3681-3688.
[23]	BLACKMORE L， ONO M， WILLIAMS B C. Chance-constrained optimal path planning with obstacles［J］. IEEE Transactions on Robotics， 2011， 27（6）： 1080-1094.
[24]	WU P C， XIE J F， CHEN J. Safe path planning for unmanned aerial vehicle under location uncertainty［C］∥2020 IEEE 16th International Conference on Control & Automation （ICCA）. Piscataway： IEEE Press， 2020： 342-347.
[25]	WAKABAYASHI T， SUZUKI S. Dynamic obstacle avoidance for multi-rotor UAV using chance-constraints based on obstacle velocity［J］. Robotics and Autonomous Systems， 2023， 160： 104320.
[26]	ZHANG X X， MA J， CHENG Z L， et al. Trajectory generation by chance-constrained nonlinear MPC with probabilistic prediction［J］. IEEE Transactions on Cybernetics， 2021， 51（7）： 3616-3629.
[27]	KURZHANSKI A B， VARAIYA P. Ellipsoidal techniques for reachability analysis［C］∥Hybrid Systems： Computation and Control. Berlin： Springer， 2000： 202-214.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References