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摘要:
针对自主运行模式下的航空器路径冲突问题，提出一种基于分布式决策的多航空器路径博弈协调方法。所提方法设计构建了自主运行航空器路径规划混合策略-演化博弈模型，分为两部分：基于混合策略博弈思想，通过剔除无法达到均衡状态的博弈策略而缩减航空器路径博弈的解空间构成，极大简化博弈问题的求解过程；将该问题设定为不完全理性的演化博弈问题，进而设定各航空器路径选择偏好的演化规律，求解该演化博弈问题的均衡解。应用实际扇区结构和数据进行仿真实验，结果表明：执行自主运行模式的航空器，其平均路径长度仅增长了9.15%，但是可以促进空中交通复杂度峰值降低30.15%，空域中得到高效利用的栅格数量增加了26.46%。在模拟晴空湍流和座舱失压扰动环境下，该路径的抗扰动能力得到显著提升，受扰航空器数量分别下降了32.39%和56.72%。
Abstract:In order to solve the problem of aircraft path conflict in autonomous operation mode, this paper proposed a multi-aircraft path game coordination method based on distributed decision-making. Firstly, a mixed strategy-evolutionary game model was constructed based on the path planning of aircraft operating in autonomous mode. The model was divided into two parts. Firstly, based on the idea of a mixed strategy game, the solution space of the aircraft path game was reduced by eliminating the game strategy that cannot reach the equilibrium state, and the solution process of the game problem was greatly simplified. Then, the game problem was regarded as an incomplete rational evolutionary game problem, and the evolution law of each aircraft path preference was set up. The equilibrium solution of the evolutionary game problem was solved. Finally, simulation experiments were carried out using the actual sector structure and data. The results show that the average path length of aircraft operating in autonomous mode only increases by 9.15%, but the peak air traffic complexity decreases by 30.15%, and the number of highly efficient grids in airspace increases by 26.46%. In the simulated environment of clear air turbulence and cabin decompression, the anti-disturbance capability of this path is significantly improved, and the number of disturbed aircraft decreases by 32.39% and 56.72%, respectively.
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图 1 传统模式(PS=0%)和自主运行模式(PS=100%)的航空器路径示意图
Figure 1. Paths of aircraft operating in traditional mode (PS=0%) and autonomous mode (PS=100%)
图 3 不同混合比对应各时间片栅格最大复杂度
Figure 3. Maximum complexity of grids corresponding to time slices with different mixing ratios
图 4 不同混合比对应的受扰航空器数量分布-晴空湍流
Figure 4. Number of disturbed aircraft at different mixing ratios for clear air turbulence
图 5 不同混合比对应的受扰航空器数量分布-座舱失压
Figure 5. Number of disturbed aircraft at different mixing ratios for cabin decompression
表 1 不同混合比对应的路径平均长度统计
Table 1. Average path length at corresponding proportion of aircraft operating in autonomous mode
模式 平均值 变化比例/% 传统运行模式(PS=0%) 62.67 混合运行模式(PS=20%) 70.44 +11.03 混合运行模式(PS=40%) 65.27 +3.99 混合运行模式(PS=60%) 63.13 +0.74 混合运行模式(PS=80%) 67.35 +6.95 自主运行模式(PS=100%) 68.98 +9.15 
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表 2 不同混合比对应复杂度指标统计
Table 2. Statistical table of complexity indexes corresponding to different mixing ratios
模式 栅格最大复杂度平均值 栅格最大复杂度变化率/% 高复杂度栅格占比平均值/% 高复杂度栅格占比变化率/% 传统运行模式(PS=0%) 1.924 3.06 混合运行模式(PS=20%) 17.18 −10.71 3.37 +9.93 混合运行模式(PS=40%) 1.628 −15.38 3.62 +18.36 混合运行模式(PS=60%) 1.477 −23.19 3.59 +17.47 混合运行模式(PS=80%) 1.421 −26.12 3.72 +21.59 自主运行模式(PS=100%) 1.344 −30.15 3.87 +26.46
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表 3 不同混合比对应蒙特卡罗仿真模拟统计
Table 3. Monte Carlo simulation corresponding to different mixing ratios
模式 扇区容量 扇区容量变化率/% 平均路径长度 平均路径长度变化率/% 无法解脱的冲突数量 传统运行模式(PS=0%) 67 62.67 混合运行模式(PS=20%) 70.10 +4.63 66.58 +6.24 0 混合运行模式(PS=40%) 72.89 +8.79 63.66 +1.58 0 混合运行模式(PS=60%) 75.32 +12.42 66.51 +6.13 0 混合运行模式(PS=80%) 77.45 +15.60 69.08 +10.23 0 自主运行模式(PS=100%) 82.17 +22.64 71.85 +14.65 0
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表 4 不同混合比对应的实验数据-晴空湍流
Table 4. Experimental data at different mixing ratios for clear air turbulence
模式 仿真
次数平均受扰
航空器数量平均受扰航空器
数量变化率/%标准差 传统运行模式(PS=0%) 50 4.97 3.06 混合运行模式(PS=20%) 50$ \times $20 4.84 −2.62 2.63 混合运行模式(PS=40%) 50$ \times $20 4.79 −3.62 2.58 混合运行模式(PS=60%) 50$ \times $20 4.32 −13.08 2.20 混合运行模式(PS=80%) 50$ \times $20 4.34 −12.68 2.18 自主运行模式(PS=100%) 50 3.43 −30.98 2.10
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表 5 受扰动影响的航空器数量-座舱失压
Table 5. Number of disturbed aircraft for cabin decompression
模式 仿真
次数平均受扰
航空器数量平均受扰航空器
数量变化率/%标准差 传统运行模式(PS=0%) 67 8.53 2.58 混合运行模式(PS=20%) 67$ \times $20 6.72 −21.22 2.66 混合运行模式(PS=40%) 67$ \times $20 5.37 −37.05 2.72 混合运行模式(PS=60%) 67$ \times $20 5.01 −41.27 2.64 混合运行模式(PS=80%) 67$ \times $20 4.46 −47.71 2.41 自主运行模式(PS=100%) 67 3.66 −57.09 2.19
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[1] International Air Transport Association. Air passenger market analysis[EB/OL]. (2024-10-31)[2024-01-10]. Montreal: IATA, 2022. [2] EUROCONTROL. European ATM master plan [EB/OL]. (2019-12-17)[2024-01-10]. Brussel: EUROCONTROL, 2015. [3] Federal Aviation Administration. NextGen implementation plan[EB/OL]. (2024-10-30)[2024-01-10]. Washington, D. C.: FAA, 2020. [4] International Civil Aviation Organization. Aviation system block upgradesthreads[EB/OL]. (2024-02-27)[2024-01-10]. Montreal: ICAO, 2020. [5] 李岱潍, 汤新民, 陆晓娜, 等. 基于冲突点到达时间的航空器自主间隔控制律[J]. 系统工程与电子技术, 2024, 46(10): 3484-3491.LI D W, TANG X M, LU X N, et al. Aircraft autonomous separation control based on time-to-go of conflict point[J]. Systems Engineering and Electronics, 2024, 46(10): 3484-3491(in Chinese). [6] 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. doi: 10.1016/j.engappai.2021.104286 [7] 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. doi: 10.1016/j.trc.2022.103701 [8] SUI D, XU W P, ZHANG K. Study on the resolution of multi-aircraft flight conflicts based on an IDQN[J]. Chinese Journal of Aeronautics, 2022, 35(2): 195-213. doi: 10.1016/j.cja.2021.03.015 [9] STRYSZOWSKI M, LONGO S, D’ALESSANDRO D, et al. A framework for self-enforced optimal interaction between connected vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(10): 6152-6161. [10] WANG H, MENG Q, CHEN S K, et al. Competitive and cooperative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach[J]. Transportation Research Part B: Methodological, 2021, 149: 322-346. doi: 10.1016/j.trb.2021.05.007 [11] LIN D C, JABARI S E. Pay for intersection priority: A free market mechanism for connected vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5138-5149. [12] AGNEL TONY L, GHOSE D, CHAKRAVARTHY A. Correlated-equilibrium-based unmanned aerial vehicle conflict resolution[J]. Journal of Aerospace Information Systems, 2022, 19(4): 283-304. doi: 10.2514/1.I011001 [13] MU L F, HAN S C. Satisficing game approach to conflict resolution for cooperative aircraft sharing airspace[J]. Big Data, 2021, 9(1): 53-62. doi: 10.1089/big.2020.0155 [14] ALBABA B M, MUSAVI N, YILDIZ Y. A 3D game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts[J]. Transportation Research Part C: Emerging Technologies, 2021, 133: 103417. doi: 10.1016/j.trc.2021.103417 [15] 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. doi: 10.2514/1.G002716 [16] TANG X M, LU X N, ZHENG P C. Aircraft autonomous separation assurance based on cooperative game theory[J]. Aerospace, 2022, 9(8): 421. doi: 10.3390/aerospace9080421 [17] QIAN X W, MAO J F, CHEN C H, et al. Coordinated multi-aircraft 4D trajectories planning considering buffer safety distance and fuel consumption optimization via pure-strategy game[J]. Transportation Research Part C: Emerging Technologies, 2017, 81: 18-35. doi: 10.1016/j.trc.2017.05.008 [18] 蒋旭瑞, 吴明功, 温祥西, 等. 基于合作博弈的多机飞行冲突解脱策略[J]. 系统工程与电子技术, 2018, 40(11): 2482-2489.JIANG X R, WU M G, WEN X X, et al. Conflict resolution of multi-aircraft based on the cooperative game[J]. Systems Engineering and Electronics, 2018, 40(11): 2482-2489(in Chinese). [19] 吴明功, 蒋旭瑞, 温祥西, 等. 军航飞机流穿越民航航线冲突探测与解脱问题[J]. 北京航空航天大学学报, 2019, 45(5): 863-872.WU M G, JIANG X R, WEN X X, et al. Conflict detection and resolution in scenario of military aircraft flow passing through civil aviation route[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(5): 863-872(in Chinese). [20] 王立超, 张洪海, 刘皞, 等. 面向意图的通用航空器冲突解脱自主决策方法[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(5): 854-858.WANG L C, ZHANG H H, LIU H, et al. Intention-oriented autonomous decision-making method for conflict resolution of general aircraft[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44(5): 854-858(in Chinese). [21] 张宏宏, 甘旭升, 辛建霖, 等. 基于合作博弈的多机冲突解脱算法[J]. 北京航空航天大学学报, 2022, 48(5): 863-871.ZHANG H H, GAN X S, XIN J L, et al. Multi-aircraft conflict resolution algorithm based on cooperative game[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 863-871(in Chinese). [22] 朱永文, 谢华, 蒲钒, 等. 空域网格化方法及其在空管中的应用研究[J]. 航空工程进展, 2021, 12(4): 12-24.ZHU Y W, XIE H, PU F, et al. Research of airspace gridding method and its application in air traffic management[J]. Advances in Aeronautical Science and Engineering, 2021, 12(4): 12-24(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] 王红勇, 郭宇鹏. 终端区离场航空器自主路径规划研究[J/OL]. 北京航空航天大学学报, 2023: 1-14.WANG H Y, GUO Y P. Research on autonomous path planning of departing aircraft in terminal area[J]. Beijing University of Aeronautics and Astronautics, 2023: 1-14(in Chinese). [25] FREY E. Evolutionary game theory: Theoretical concepts and applications to microbial communities[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(20): 4265-4298. doi: 10.1016/j.physa.2010.02.047 [26] International Air Transport Association. 2022 airline safety performance[EB/OL]. (2023-03-07)[2024-01-10]. Montreal: IATA, 2022. -
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