Planning method for three-dimensional air route network of urban low-altitude logistics drones
Article Text (Baidu Translation)
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摘要: 为规范城市低空物流无人机规模化运行秩序，提出了城市低空物流无人机立体航路网络规划方法。基于城市低空分层空域结构，采用栅格法对城市低空进行离散化建模；结合“一网-双层-三节点”航路网络架构，构建了转运点选址模型与航路网络规划模型；搭建了基于自组织映射神经网络与多目标模拟退火的算法框架，优化转运点空间布局与网络拓扑结构。以南京某地区为例进行仿真试验，结果表明：当需求点数量一定时，由于转运层承担了货物的流转功能，转运网络的节点平均度大于配送网络；与遗传算法与灰狼优化算法相比，提出的多目标模拟退火算法可以更好地兼顾目标之间的关系，综合得分提升17%以上；随着需求点规模不断扩大，转运点数量呈现增长趋势，激活了更多可选航路节点，拓展了无人机航路选择维度；与单层网络相比，所构建航路网络的非直线系数减少14.09%，航路流量均值降低52.43%，可有效分散航路负载。所提方法可以实现用户需求密集场景下的航路网络规划，提升了物流无人机在复杂城市环境中的运行可行性。Abstract: To regulate the large-scale operational order of urban low-altitude logistics drones, a planning method for the three-dimensional air route network of urban low-altitude logistics drones was proposed. Based on the urban low-altitude layered airspace structure, the grid method was adopted for the discretization modeling of urban low-altitude airspace. Combined with the "one-network, two-layer, and three-node" route network architecture, a transfer node location model and a route network planning model were established. An algorithm framework based on a self-organizing map neural network and multi-objective simulated annealing was developed to optimize the spatial layout of transfer nodes and network topology. A simulation experiment was conducted in a certain area of Nanjing City. The results show that when the number of demand nodes is fixed, the average node degree of the transfer network is greater than that of the delivery network due to the cargo circulation function undertaken by the transfer layer. Compared with the genetic algorithm and grey wolf optimization algorithm, the proposed multi-objective simulated annealing algorithm can better balance the relationship among objectives, and the comprehensive score increases by over 17%. As the scale of demand nodes expands, the number of transfer nodes shows an increasing trend, which activates more optional route nodes and expands the dimension of drone route selection. Compared with the single-layer network, the proposed route network reduces the non-linear coefficient by 14.09% and the average route flow by 52.43%, which can effectively disperse route loads. The proposed method enables route network planning in scenarios with dense user demands and improves the operational feasibility of logistics drones in complex urban environments.
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图 1 栅格法空域环境表征示意
Figure 1. Schematic of airspace environment representation by grid method
图 2 物流无人机立体航路网络整体架构
Figure 2. Overall architecture of three-dimensional route network for logistics drone
图 7 转运点初始选址服务距离与得分
Figure 7. Service distance and score of transfer node initial location
图 14 不同需求规模下的航路流量分布
Figure 14. Route traffic distribution under different demand scales
图 18 不同结构下的航路流量分布
Figure 18. Route traffic flow distribution under different structures
表 1 仿真参数
Table 1. Simulation parameters
参数 取值 无人机满载续航里程/km 8 无人机空载续航里程/km 10 无人机最大负载质量/kg 30 无人机水平飞行速度/(m·s-1) 12 无人机垂直飞行速度/(m·s-1) 5 转运点服务半径/km 1 转运点服务压力上限 10 转运点服务压力下限 1 配送层网络高度/m 50 转运层网络高度/m 80 
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表 2 航路网络特征参数
Table 2. Characteristic parameters of the network
参数类别 参数名称 参数数值 航路网络长度 整体航路网络/km 66.40 转运层网络/km 33.24 配送层网络/km 29.90 航路网络结构 网络非直线系数 1.28 航路平均介数 37.18 转运层节点平均度 5.57 配送层节点平均度 3.27
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表 3 不同需求规模下的航路网络规划结果
Table 3. Planning results of route networks under different demand scales
需求点数量 10 15 20 25 30 35 40 45 50 需求规模 配送量/kg 2 475 3 625 4 777 6 083 7 378 8 554 9 665 10 877 12 116 转运点 转运点数量 5 5 7 6 9 7 10 9 12 服务需求点均值 3.00 4.00 3.57 5.00 3.89 5.71 4.51 5.56 4.58 网络长度/kg 整体航路网络 23.10 25.97 31.39 32.69 43.57 46.00 54.15 57.57 66.40 转运层网络 6.40 12.68 15.74 20.82 20.29 29.52 26.70 34.06 33.24 配送层网络 15.64 11.99 14.04 10.04 21.11 14.12 24.75 20.58 29.90 航路介数 均值 8.82 13.34 20.10 21.48 23.05 25.70 32.70 34.25 37.18 标准差 6.64 13.61 20.95 27.63 23.72 33.71 34.22 40.15 39.69 节点度 均值 3.76 3.87 4.10 4.13 4.34 4.36 4.50 4.52 4.62 标准差 1.55 1.45 1.64 1.76 2.20 2.14 2.34 2.38 2.77 航路流量/veh 均值 48.85 46.95 61.58 43.03 46.84 34.46 45.62 35.44 41.09 标准差 32.09 40.84 52.45 45.46 41.79 41.10 34.66 38.36 41.95 飞行时间/s 均值 159.52 161.50 170.99 164.41 151.73 148.18 157.36 158.43 155.05 标准差 70.10 73.13 77.11 72.31 59.85 58.58 65.04 65.89 62.54
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表 4 不同结构下的航路网络规划结果
Table 4. Route network planning results under different structures
参数 单层网络 双层直连 双层互连 网络长度/km 38.77 46.68 66.40 网络非直线系数 1.49 1.37 1.28 航路介数 均值 69.61 57.59 37.18 标准差 75.31 34.35 39.69 节点度 均值 3.89 2.91 4.62 标准差 0.89 3.52 2.77 航路流量/veh 均值 86.38 74.19 41.09 标准差 96.93 52.47 41.95 飞行时间/s 均值 191.72 164.43 155.05 标准差 86.36 68.52 62.54 任务累计里程/107 m 2.41 2.06 1.81
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