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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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