基于人群密度风险的无人机动态路径规划研究

doi:10.18306/dlkxjz.2021.09.007

Abstract

Abstract:

With the rapid growth of the number of unmanned aerial vehicles (UAVs), in order to assess the risk of people on the ground especially in cities, improve the operational efficiency of UAV, and reduce the threat of UAV to the crowd, it is necessary to make a specific path planning for UAVs based on the crowd density. However, static demographic data are still used as the ground risk analysis basis for the path planning of UAV, and real-time path planning of UAV according to the spatial-temporal characteristics of crowd density are often not performed. This study first analyzed the characteristics of urban road network crowd density based on the spatiotemporal data. Second, the convolutional neural network-long short-term memory (CNN-LSTM) combined model (C-Snet model) was established to predict the population density in different urban areas. Finally, the improved A* algorithm was used for real-time path planning and risk assessment of UAV according to the predicted crowd density data. The results show that the risk of UAV operation is reduced by 76%, which can provide a theoretical reference for the development of real-time path planning function of UAV traffic management system.

Key words: UAV traffic management, risk assessment, CNN, crowd density, path planning

JIAO Qingyu, CHEN Xinfeng, ZHENG Zhigang, BAI Yiqin, LIU Yansi, ZHANG Zhengjuan, SUN Longni. Dynamic path planning of unmanned aerial vehicle based on crowd density prediction[J].PROGRESS IN GEOGRAPHY, 2021, 40(9): 1516-1527.

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指标	无人机尺寸/m
指标	1	3	8	>8
动能	<700 J	<34 kJ	<1084 kJ	>1084 kJ
运行场景
视距内运行(VLOS),受控区域且人口稀少	1	2	3	4
视距外运行(BVLOS),受控区域且人口稀少	2	3	4	5
VLOS,人口居住区	3	4	5	6
BVLOS,受控区域且在人口居住区	4	5	6	8
BVLOS,人口居住区	5	6	8	10
VLOS,密集人群	7
BVLOS,密集人群	8

隐藏层	卷积核数量	卷积核大小
卷积层 Conv1	32	2×2
Conv2	64	2×2
池化层 Maxpooling	512	4×4
全连接层 FC	512	512×1

编号	观测时间	中心点经度/(°E)	中心点纬度/(°N)	范围面积/m²	区域人群密度指数
1	2020-01-17T01:00	116.201	39.906	90062	2.9
2	2020-01-17T03:00	116.311	39.933	100235	1.0
3	2020-01-17T09:00	116.251	39.920	73640	6.5

epoch	学习速率(lr)
	MSE (标准化)
	0.1		0.01	0.001
100		0.98	0.22	0.22
500		1.00	0.19	0.15
1000		0.63	0.14	0.08

Conv1	Conv2	Maxpooling	MSE
3×3	3×3	3×3	14.7
5×5	3×3	3×3	10.5
7×7	3×3	3×3	12.3

Dynamic path planning of unmanned aerial vehicle based on crowd density prediction

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神经元数量	MSE	拟合速率/s
64-64-64	15.48(欠拟合)	1048.06
256-256-256	7.03	1326.51
256-128-64	7.07	1023.37

模型	R²	MSE	MAE
SVM	0.73	4.02	18.30
RF	0.82	3.77	10.25
Multi regression	0.70	14.23	22.30
C-Snet	0.86	3.78	5.32

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