基于风险意识的eVTOL自主避让

  • 张康 ,
  • 汤新民 ,
  • 顾俊伟
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  • 1. 南京航空航天大学
    2. 中国民航大学

收稿日期: 2025-04-07

  修回日期: 2025-07-07

  网络出版日期: 2025-07-15

基金资助

新一代空中交通系统中的多阶段多模式间隔控制方法研究;高密度混合运行条件下的异构电动垂直起隆飞行器自主感知与避让研究;城市空中交通UAM飞行控制及通信导航监视技术

Risk-aware autonomous avoidance for eVTOL

  • ZHANG Kang ,
  • TANG Xin-Min ,
  • GU Jun-Wei
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Received date: 2025-04-07

  Revised date: 2025-07-07

  Online published: 2025-07-15

摘要

在城市空中交通背景下,目前针对动态、不确定环境下电动垂直起降(electric Vertical Take-Off and Landing,eVTOL)飞机的实时避让还没有一个成熟的解决方案,对此提出了一种具有风险意识的高效运动规划方法,用于实现eVTOL的实时避让。同时考虑eVTOL的动态碰撞规避约束和静态地理围栏约束,建立了机会约束的模型预测控制问题(CC-MPC)作为运动规划的子问题;利用Big-M和置信椭球重构机会约束,将CC-MPC转换为混合整数规划问题(MIP),接着通过迭代凸化的优化方法来快速求解MIP,并在优化过程中加入全局搜索算法作为前端以提供高质量的初始方案;最后通过滚动时域框架整合所有过程,实现了eVTOL动态轨迹的快速生成,在仿真实验中通过实现不同场景下的规划过程,验证了所提方法的有效性。

本文引用格式

张康 , 汤新民 , 顾俊伟 . 基于风险意识的eVTOL自主避让[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.32083

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 effi-cient motion planning method for real-time eVTOL avoidance. The problem is formulated as a model predictive control (MPC) framework with chance constraints (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 solve the MIP efficiently, 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. The effectiveness of the proposed approach is demonstrated through simulation experiments across various flight scenarios.

参考文献

[1] L. GIPSON, “Nasa embraces urban air mobility, calls for market study,” https: //www.nasa.gov/aero/nasa-embraces-urban-air-mobility, 2017, accessed: 2020-02- 15.
[2] J. HOLDEN AND N. GOEL, “Fast-forwarding to a future of on-demand urban air transportation,” San Francisco, CA, 2016.
[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(S1):26-4.
YU S S, CHEN X Y. Key technological innovations and challenges in urban air mobility[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(S1): 26-47(in Chinese).
[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(05):55-77.
DENG J H. Technical status and development of electric vertical take-off and landing aircraft[J]. Acta Aeronauti-ca et Astronautica Sinica, 2024,45(05):55-77. (in Chi-nese).
[5] 吕洋,康童娜,潘泉,等.无人机感知与规避: 概念、技术与系统. 中国科学: 信息科学, 2019, 49(05): 520-537.
LYU Y, KANG T N, PAN Q, et al. UAV sense and avoidance: concepts, technologies, and systems[J]. Sci Sin Inform, 2019, 49(05): 520-537.
[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(06):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 and Astronautics, 2024,56(06):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(04):1-5.
JING X N, LIANG X L, ZHANG J Q, et al. Analysis of UAV Sense and Avoid Technology. Fire Control & Command Control, 2017, 42(04):1-5.
[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(01):275-290.
WANG X L, WANG Y J. Safety interval evaluation for multi-aircraft eVTOL in urban low altitude[J]. Acta Aero-nautica et Astronautica Sinica, 2025, 46(01): 275-290(in Chinese)
[9] 董磊,宋文佳,陈曦,等.面向预期功能安全的eVTOL智能避障系统运行时保证方法[J].中国安全科学学报,2024,34(07):105-112.
DONG L, SONG W, CHEN X, et al. Runtime assur-ance method for eVTOL intelligent obstacle avoidance system toward safety of intended functionality[J]. Chi-na Safety Science Journal, 2024,34(7): 105-112.
[10] ZOU Y, ZHANG H, ZHONG G, et al. Collision proba-bility estimation for small unmanned aircraft sys-tems[J]. Reliability Engineering & System Safety, 2021, 213: 107619.
[11] 李安醍,李诚龙,武丁杰,等.结合跳点引导的无人机随机搜索避撞决策方法[J].航空学报,2020,41(08):325-337.
LI A T, LI C L, WU D J, et al. Collision avoidance de? cision method for UAVs in random search combined with jump point guidance[J]. Acta Aeronautica et As-tronautica Sinica, 2020, 41(8): 325-337.(in Chinese)
[12] 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/OL].航空学报,1-13[2025-03-12].http://kns.cnki.net/kcms/detail/11.1929.V.20250110.1416.006.html.
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, 1-13[2025-03-12]. (Chinese)
[13] 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).
[14] KARAMAN S, WALTER M R, PEREZ A, et al. Any-time motion planning using the RRT[C]//2011 IEEE in-ternational conference on robotics and automation. IEEE, 2011: 1478-1483.
[15] HSU D, LATOMBE J C, KURNIAWATI H. On the probabilistic foundations of probabilistic roadmap plan-ning[C]//Robotics Research: Results of the 12th Interna-tional Symposium ISRR. Springer Berlin Heidelberg, 2007: 83-97.
[16] BETTS J T, CAMPBELL S, DIGIROLAMO C. Exami-nation of solving optimal control problems with delays using GPOPS-Ⅱ[J]. Numerical Algebra, Control and Optimization, 2020, 11(2): 283-305.
[17] OSBORNE M R. On shooting methods for boundary value problems[J]. Journal of mathematical analysis and applications, 1969, 27(2): 417-433.
[18] LI M, CHEN J, LIU W, et al. A Comparative of eVTOL Aircraft Path Planning Algorithms for Urban Air Mo-bility[J]. Journal of Xihua University (Natural Science Edition), 2023, 42(5): 54-61.
[19] LI Y, LIU M. Path planning of electric VTOL UAV considering minimum energy consumption in urban are-as[J]. Sustainability, 2022, 14(20): 13421.
[20] YANG X, WEI P. Scalable multi-agent computational guidance with separation assurance for autonomous ur-ban air mobility[J]. Journal of Guidance, Control, and Dynamics, 2020, 43(8): 1473-1486.
[21] CHEN M, TOMLIN C J. Hamilton–jacobi reachability: Some recent theoretical advances and applications in unmanned airspace management[J]. Annual Review of Control, Robotics, and Autonomous Systems, 2018, 1(1): 333-358.
[22] SHETTY A, GAO G X. Predicting state uncertainty bounds using non-linear stochastic reachability analysis for urban GNSS-based UAS navigation[J]. IEEE Trans-actions on Intelligent Transportation Systems, 2020, 22(9): 5952-5961.
[23] 杨建航,张福彪,王江.基于可达集的无人机低空飞行冲突解脱算法[J].北京航空航天大学学报,2023, 49(07): 1813-1827.
YANG J H, ZHANG F B, WANG J. Conflict resolution algorithms for UAV low-altitude flight based on reacha-ble set[J]. Journal of Beijing University of Aeronautics and Astronautics,2023, 49(07): 1813-1827 (in Chinese).
[24] HSU T W, CHOI J J, AMIN D, et al. Towards flight envelope protection for the NASA tiltwing eVTOL flight mode transition using Hamilton-Jacobi reachabil-ity[J]. Journal of the American Helicopter Society, 2024, 69(2): 1-18.
[25] JANSON L, SCHMERLING E, PAVONE M. Monte Carlo motion planning for robot trajectory optimization under uncertainty[M]//Robotics Research: Volume 2. Cham: Springer International Publishing, 2017: 343-361.
[26] CALAFIORE G, CAMPI M C. Uncertain convex pro-grams: randomized solutions and confidence levels[J]. Mathematical Programming, 2005, 102: 25-46.
[27] 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.
[28] WU 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.
[29] LUDERS B, KOTHARI M, HOW J. Chance con-strained RRT for probabilistic robustness to environ-mental uncertainty[C]//AIAA guidance, navigation, and control conference. 2010: 8160.
[30] SAFAOUI S, GRAVELL B J, RENGANATHAN V, et al. Risk-Averse RRT Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Sys-tems Under Uncertainty[C]//2021 IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems (IROS). IEEE, 2021: 3681-3688.
[31] BLACKMORE L, ONO M, WILLIAMS B C. Chance-constrained optimal path planning with obstacles[J]. IEEE Transactions on Robotics, 2011, 27(6): 1080-1094.
[32] WU P, XIE J, CHEN J. Safe path planning for un-manned aerial vehicle under location uncertain-ty[C]//2020 IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 2020: 342-347.
[33] WAKABAYASHI T, SUZUKI Y, SUZUKI S. Dynamic obstacle avoidance for Multi-rotor UAV using chance-constraints based on obstacle velocity[J]. Robotics and Autonomous Systems, 2023, 160: 104320.
[34] ZHANG X, MA J, CHENG Z, et al. Trajectory genera-tion by chance-constrained nonlinear MPC with proba-bilistic prediction[J]. IEEE Transactions on Cybernetics, 2020, 51(7): 3616-3629.
[35] DA SILVA ARANTES M, TOLEDO C F M, WILLIAMS B C, et al. Collision-free encoding for chance-constrained nonconvex path planning[J]. IEEE Transactions on Robotics, 2019, 35(2): 433-448.
[36] CHAI R, TSOURDOS A, SAVVARIS A, et al. Fast generation of chance-constrained flight trajectory for unmanned vehicles[J]. IEEE Transactions on Aero-space and Electronic Systems, 2020, 57(2): 1028-1045.
[37] KURZHANSKI A B, VARAIYA P. Ellipsoidal tech-niques for reachability analysis[C]//International work-shop on hybrid systems: Computation and control. Ber-lin, Heidelberg: Springer Berlin Heidelberg, 2000: 202-214.
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