| Citation: | WANG H Y，GUO Y P. Autonomous path planning of departing aircraft in terminal area[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（2）：446-456 （in Chinese）
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With the gradual development of the aircraft self-separation operation and the continuous climbing operation (CCO) mode, it can effectively solve the problem that the departure path of aircraft in the current terminal area is fixed and single, which leads to the low operational efficiency of airspace. Therefore, an autonomous path planning method based on artificial potential field-particle swarm optimization（APF-PSO） algorithm was proposed in this paper. To guarantee operation safety, the airspace environment was first rasterized, the aircraft autonomous operation mode was taken into consideration, and the airspace complexity of each grid was computed. This prevented departing aircraft from flying into high-complexity grids. The aircraft climbing performance constraint model was constructed based on the BADA database and reduced force climbing mode. Then the path planning was carried out by using the APF-PSO algorithm of artificial potential field（APF） and particle swarm optimization（PSO） algorithm, and the local extremum-target unreachable problem inherent in the artificial potential field method was solved by using the region search algorithm of particle swarm optimization. The Bessel curve method was used to optimize the path planning and the concept of sliding time window was introduced to optimize the departure time of aircraft. Finally, using the actual structure and operation data of Shanghai terminal airspace, the proposed method was applied to simulate. The simulation test results show that the APF-PSO algorithm can effectively generate the aircraft conflict-free departure path and avoid busy airspace. The optimized path satisfies the aircraft climbing performance constraints and is better than the actual path (path length reduced by 23.78%, maximum turning rate reduced by 55.73%, maximum climbing rate reduced by 9.94%). Additionally, the autonomous operation mode of departing aircraft results in a more balanced airspace operation condition than the actual operating mode (a reduction of 3.92% in peak grid complexity), which can significantly increase the airspace utilization rate.
| [1] |
International Civil Aviation Organization. Global air traffic management operational concept[EB/OL]. (2012-08-28)[2023-02-15]. https://www.icao.int/Meetings/anconf12/Document%20Archive/9854_cons_en%5B1%5D.pdf.
|
| [2] |
Federal Aviation Administration. NextGen implementation plan-FAA[EB/OL]. (2022-07-15)[2023-02-15]. https://www.faa.gov/nextgen/2018-2019-nextgen-implementation-plan.
|
| [3] |
DEGAS A, KADDOUM E, GLEIZES M P, et al. Cooperative multi-agent model for collision avoidance applied to air traffic management[J]. Engineering Applications of Artificial Intelligence, 2021, 102: 104286.
|
| [4] |
蔡开泉, 赵鹏. 空中交通自主间隔管控技术研究[J]. 南京航空航天大学学报, 2022, 54(4): 688-699.
CAI K Q, ZHAO P. Key technologies on air traffic self-separation management[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2022, 54(4): 688-699(in Chinese).
|
| [5] |
CHEN Y T, HU M H, YANG L. Autonomous planning of optimal four-dimensional trajectory for real-time en-route airspace operation with solution space visualisation[J]. Transportation Research Part C: Emerging Technologies, 2022, 140: 103701.
|
| [6] |
张洪海, 汤一文, 许炎. TBO模式下终端区进场交通流优化模型与仿真分析[J]. 航空学报, 2020, 41(7): 323844.
ZHANG H H, TANG Y W, XU Y. Optimizing arrival traffic flow in airport terminal airspace under trajectory based operations[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7): 323844(in Chinese).
|
| [7] |
International Civil Aviation Organization. Global, continuous climb operations (CCO) manual. [EB/OL]. (2013-10-25)[2023-02-16]. https://applications.icao.int/tools/ATMiKIT/story_content/external_files/10260008117raft_en_CCO.pdf.
|
| [8] |
ALLIGIER R, GIANAZZA D. Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study[J]. Transportation Research Part C: Emerging Technologies, 2018, 96: 72-95.
|
| [9] |
杜卓铭, 张军峰, 苗洪连, 等. 考虑推力意图的航空器连续爬升垂直剖面预测[J]. 北京航空航天大学学报, 2024, 50(4): 1347-1353.
DU Z M, ZHANG J F, MIAO H L, et al. Aircraft vertical profile prediction for continuous climb based on thrust intention[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50(4): 1347-1353(in Chinese).
|
| [10] |
DESHMUKH R, SUN D W, KIM K, et al. Temporal logic learning-based anomaly detection in metroplex terminal airspace operations[J]. Transportation Research Part C: Emerging Technologies, 2021, 126: 103036.
|
| [11] |
MURÇA M C R, HANSMAN R J. Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support[J]. Transportation Research Part C: Emerging Technologies, 2018, 97: 301-323.
|
| [12] |
GONZÁLEZ-ARRIBAS D, SOLER M, SANJURJO-RIVO M, et al. Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms[J]. Aerospace Science and Technology, 2019, 89: 445-459.
|
| [13] |
LIN Y C, SARIPALLI S. Sampling-based path planning for UAV collision avoidance[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3179-3192.
|
| [14] |
WOO J W, AN J Y, CHO M G, et al. Integration of path planning, trajectory generation and trajectory tracking control for aircraft mission autonomy[J]. Aerospace Science and Technology, 2021, 118: 107014.
|
| [15] |
DU Y S, ZHANG X J, NIE Z L. A real-time collision avoidance strategy in dynamic airspace based on dynamic artificial potential field algorithm[J]. IEEE Access, 2019, 7: 169469-169479.
|
| [16] |
CHEN L L, HAN S H, DU C K, et al. A real-time integrated optimization of the aircraft holding time and rerouting under risk area[J]. Annals of Operations Research, 2022, 310(1): 7-26.
|
| [17] |
张大志, 刘万辉, 缪存孝, 等. 全向移动机器人动态避障方法[J]. 北京航空航天大学学报, 2021, 47(6): 1115-1123.
ZHANG D Z, LIU W H, MIAO C X, et al. Dynamic obstacle avoidance method for omnidirectional mobile robots[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1115-1123(in Chinese).
|
| [18] |
韩尧, 李少华. 基于改进人工势场法的无人机航迹规划[J]. 系统工程与电子技术, 2021, 43(11): 3305-3311.
HAN Y, LI S H. UAV path planning based on improved artificial potential field[J]. Systems Engineering and Electronics, 2021, 43(11): 3305-3311(in Chinese).
|
| [19] |
LIU Y, ZHANG X J, ZHANG Y, et al. Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach[J]. Chinese Journal of Aeronautics, 2019, 32(6): 1504-1519.
|
| [20] |
徐钊, 胡劲文, 马云红, 等. 无人机碰撞规避路径规划算法研究[J]. 西北工业大学学报, 2019, 37(1): 100-106.
XU Z, HU J W, MA Y H, et al. A study on path planning algorithms of UAV collision avoidance[J]. Journal of Northwestern Polytechnical University, 2019, 37(1): 100-106(in Chinese).
|
| [21] |
朱永文, 陈志杰, 蒲钒, 等. 数字化空域系统发展研究[J]. 中国工程科学, 2021, 23(3): 135-143.
ZHU Y W, CHEN Z J, PU F, et al. Development of digital airspace system[J]. Strategic Study of CAE, 2021, 23(3): 135-143(in Chinese).
|
| [22] |
中华人民共和国交通运输部. 民用航空空中交通管理规则[EB/OL]. (2017-09-29)[2023-02-17]. https://xxgk.mot.gov.cn/2020/gz/202303/W020230426346468961118.pdf.
Ministry of Transport of the People's Republic of China. Civil aviation air traffic management regulations[EB/OL]. (2017-09-29)[2023-02-17]. https://xxgk.mot.gov.cn/2020/gz/202303/W020230426346468961118.pdf(in Chinese).
|
| [23] |
王红勇, 郭宇鹏. 基于航空器自主运行的空中交通复杂性建模[J]. 交通运输系统工程与信息, 2022, 22(2): 305-312.
WANG H Y, GUO Y P. Air traffic complexity model based on aircraft self-separation operation[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 305-312(in Chinese).
|
| [24] |
PARK S G, MENON P K. Game-theoretic trajectory-negotiation mechanism for merging air traffic management[J]. Journal of Guidance, Control, and Dynamics, 2017, 40(12): 3061-3074.
|
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