A Study on Parallel Routes Lateral Separation for Urban Logistics UAV
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摘要: 面向未来城市物流无人机高密飞行下安全运行需求，选取空域利用率高的平行航线，聚焦平行航线运行中碰撞风险，研究同时考虑冲突频率和碰撞概率的间隔模型，提出符合安全要求的平行航线横向间隔。借鉴有人机管制经验，确定安全目标水平，并以平行航线上相邻无人机纵向距离小于纵向间隔视为冲突评判标准，再根据实际运行数据，计算区域无人机冲突实际发生频率。综合考虑无人机参数、飞行流量、空域性能、偏离概率及角度、冲突探测与解脱机制等因素，构建多参数、多约束的平行航线城市物流无人机运行场景模拟平台。引入蒙特卡洛方法，给定冲突发生频率和意外偏离概率，假设无人机以一定概率因随机意外偏离而引发无人机之间飞行冲突，模拟冲突下无人机的运行结果，记录违反测试标准的事件。设定1~51 m多个横向间隔待选值，实施510万次仿真实验并统计实验结果，结果发现：①发生违反测试标准事件共计50 302起；②事故发生的概率密度与横向间隔服从负指数分布；③可采用施加合适的横向间隔策略，实现安全目标水平。基于拟合的概率密度函数，通过计算剩余风险，对比安全目标水平，确定城市物流无人机平行航线横向间隔为33米。Abstract: To improve the operational safety of urban logistics with unmanned aerial vehicles (UAV) in high-density regions, lateral separation (LS) for UAVs in parallel routes is studied. Specifically, the conflict frequency and collision probability are taken into account in the LS model and the LS for UAVs meeting the safety requirements is determined. The target level of safety (TLS) is set and the criteria of conflict is defined as the lateral distance of adjacent UAVs is less than the required LS. Then, the frequency of conflict can be counted by using the real operation data of UAVs. An urban logistics system using UAVs is simulated, comprehensively incorporating the UAVs' performances, flight flow, airspace volume, deviation probability, deviation angle, conflict detection and resolution, etc. The Monte Carlo method is introduced to assess the probability of collision by simulating the scenarios where a UAV has a random accidental deviation causing conflict with the adjacent UAVs under a given probability. A total of 5.1 million rounds of simulations are performed and the LS ranges from 1 meter to 51 meter. The results show that: ①50 302 violations are observed; ②the probability density function (PDF) of collision and the LS value fit an exponential distribution, well; ③applying a proper lateral separation would be a potential risk mitigation strategy for reaching TSL. In summary, the required lateral separation for UAVs in the urban city is suggested as 33 m by comparing the accepted TSL with the residual risk calculated by the fitted PDF.
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表 1 物流无人机参数
Table 1. Logistics UAV parameters
参数 符号 取值 纵向尺寸/m λx 2.5 横向尺寸/m λy 2.5 高度尺寸/m λz 0.6 飞行速度/(m/s) v 12 飞行高度/m h 253 高度误差标准差 σz 1.389 5 位置误差标准差 σxy 2.348 小时流量期望/h 30 
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表 2 仿真参数
Table 2. Simulation parameters
参数释义 参数取值 Ui无人机初始位置/m (0, 0, h) (xn0, yn0, h) Un无人机初始位置/m xn0~U(-150, 150) yn0~[1, 51] 各yn0仿真次数/10 000 10 单次仿真时长/s 60 统计步长/s 1 无人机高度/m 253 无人机速度/(m/s) 12 Ui无人机偏离时刻/s tdev~U(1, 60) Ui无人机偏离时长/s 3 Ui无人机偏离角度/(°) w~N(45, 52)
w∈[40, 60]Ui无人机避让角度/(°) -90 高度误差标准差/m 1.389 5 位置误差标准差/m 2.348 0
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表 3 TCV数据
Table 3. TCV data
间隔/m TCV/次 间隔/m TCV/次 间隔/m TCV/次 间隔/m TCV/次 1 3 767 14 1 634 27 460 40 216 2 3 588 15 1 122 28 384 41 235 3 2 981 16 1 048 29 455 42 142 4 2 991 17 1 424 30 451 43 201 5 2 172 18 972 31 390 44 178 6 2 535 19 882 32 353 45 177 7 2 379 20 774 33 340 46 93 8 2 383 21 1 030 34 280 47 98 9 2 118 22 758 35 306 48 127 10 1 808 23 642 36 255 49 133 11 1 443 24 612 37 273 50 93 12 1 896 25 671 38 236 51 110 13 1 823 26 587 39 276
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