基于速度随机分布的低空空域小型无人机碰撞风险评估模型

王莉莉; 阳杰

doi:10.3963/j.jssn.1674-4861.2022.04.007

基于速度随机分布的低空空域小型无人机碰撞风险评估模型

doi: 10.3963/j.jssn.1674-4861.2022.04.007

王莉莉^,,
阳杰

中国民航大学空中交通管理学院天津 300300

基金项目:

国家自然科学基金委员会与中国民用航空局联合资助项目 U1633124

详细信息

通讯作者:
王莉莉（1973—），博士，教授. 研究方向：空中交通流量管理、航空器运行安全评估. E-mail: llwang317@163.com

中图分类号: X949; V279
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出版历程
- 收稿日期: 2022-01-04
- 网络出版日期: 2022-09-17

A Collision Risk Model for Small UAVs Based on Velocity Random Distribution in Low-altitude Airspace

WANG Lili^,,
YANG Jie

College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 碰撞风险是评价航空器运行安全性、确定航空器运行条件的关键指标。针对低空空域小型无人机数量增多导致空域安全隐患增加的问题，提出了1种基于速度随机分布的碰撞风险评估模型，确定了无人机在空域中的安全运行的条件。根据低空空域小型无人机操纵性灵活的特点，提出了针对低空空域小型无人机不同飞行动作的碰撞模板：为自由飞行的无人机设置符合实际运行的含碰撞层和避险层的双层球体碰撞模板；为沿固定路径飞行的无人机设置以机身尺寸为参考的长方体碰撞模板。考虑无人机飞行方向和速度变化快的特点，将传统无人机速度的线性分布模型改进为随机分布模型，计算无人机的相对运动关系，再利用速度矢量法计算碰撞模板扫过的空间体积。引入无人机动态定位误差、速度误差，在传统人机可靠性的基础上，建立基于速度随机分布的低空空域小型无人机碰撞风险评估模型。选取大疆M300和M600这2种型号的无人机作为验证机型，运用Matlab软件模拟特定空域场景，并分析碰撞风险与小型无人机密度的关系。通过仿真可以发现：空域内碰撞风险与无人机密度呈正相关关系；根据国际民用航空组织空域安全标准，2种验证机型安全运行的最大密度分别为4.2架/km³和5.0架/km³；在满足安全运行条件的前提下，采用新的碰撞风险评估模型，空域容纳2种无人机的数量的密度上限可分别提高106.9%和88.7%。实验结果表明，新的碰撞风险模型更加符合小型无人机运行特征，未来可以用于提升空域内无人机容量、提升空域利用率和无人机运行效率。
- 航空器运行安全 /
- 无人机 /
- 碰撞风险模型 /
- 随机分布 /
- 双碰撞模板 /
- 低空空域
Abstract: Collision risk is a key indicator to evaluate the safety of aircraft and the main factor to determine the aircraft's operating conditions in the airspace. To handle the potential conflict due to the increasing number of small Unmanned Aerial Vehicles (UAVs) in low-altitude airspace, a novel collision risk model based on velocity random distribution is developed to determine the safe operating conditions of UAVs in low-altitude airspaces. New collision templates for UAVs are proposed, incorporating the maneuverability and flexibility of small UAVs. For a free-flying UAV, a double-layer sphere collision template is developed, including a collision layer and an avoidance layer. For a UAV following a fixed path, a cuboid collision template is proposed, incorporating the fuselage size of the UAV. Considering the rapid change of course and speed of the UAV, a stochastic velocity model is adopted instead of a linear model, and the relative velocity between UAVs is calculated, which is used to determine the space swept by the collision template. Considering positioning errors and speed errors of UAVs, the collision risk model based on velocity random distribution is proposed for UAVs in low-altitude airspace. Two types of UAVs, DJI M300 and M600, are selected as verification models. The Matlab software is used to simulate specific airspace scenarios. Then the relationships between collision risk and the density of small UAVs are analyzed. The simulations show that the collision risk in the airspace is positively correlated with the density of UAVs. According to the safety standards from the International Civil Aviation Organization, the maximum densities for the safe operation of the two types of verification models are 4.2 aircraft/km³ and 5.0 aircraft/km³, respectively. Under the premise of satisfying the safe conditions, the proposed model can increase the upper limit of the density of the two types of UAVs in the airspace by 106.9% and 88.7%, respectively. The results reveal that the proposed model is more consistent with the operating characteristics of UAVs. It can be used to improve the utilization of airspace, increase the capacity of UAVs in the airspace, and improve their operational efficiency in the future.
- safety of aircraft operation /
- unmanned aerial vehicle (UAV) /
- collision risk model /
- random distribution /
- double-layer collision template /
- low-altitude airspace

HTML全文

图 1 碰撞模板

Figure 1. Collision template

下载: 全尺寸图片幻灯片

图 2 碰撞过程示意图

Figure 2. Schematic diagram of collision process

下载: 全尺寸图片幻灯片

图 3 碰撞风险与无人机密度的关系

Figure 3. Relationship between collision risk and UAV density

下载: 全尺寸图片幻灯片

表 1 M600无人机相关参数

Table 1. Related parameters of UAV M600

参数	取值	参数	取值	参数	取值
V_max/(m/s)	18	μ_v	0	λ_x/m	1.668
V_min/(m/s)	3	σ_g²	6.6	λ_y/m	1.518
μ_g	0	σ_v²	0.072 3	λ_z/m	0.759

下载: 导出CSV

表 2 M300无人机相关参数

Table 2. Related parameters of UAV M300

参数	取值	参数	取值	参数	取值
V_max/(m/s)	23	μ_v	0	λ_x/m	0.81
V_min/(m/s)	5	σ_g²	6.6	λ_y/m	0.67
μ_g	0	σ_v²	0.057 5	λ_z/m	0.43

下载: 导出CSV

表 3 模型计算相关参数

Table 3. Parameters related to model calculations

参数	取值
k₁	0.900 00
k₂	0.000 85
k₃	0.903 80

下载: 导出CSV

表 4 结果对比分析

Table 4. Comparative analysis of results

模型	碰撞模板	无人机速度分布	机型	碰撞风险与无人机密度关系	最大安全密度/(架/km³)
有人机模型	长方体	线性分布	M600	线性	2.03
有人机模型	长方体	线性分布	M300	线性	2.65
改进无人机模型	长方体+双层球体	随机分布	M600	非线性	4.2
改进无人机模型	长方体+双层球体	随机分布	M300	非线性	5.0

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参考文献(20)

[1]	王智显, 张雁平, 詹雨飞. 面向低慢小无人机的SKDC-LDS技术[J]. 电子信息对抗技术, 2021, 36(3): 72-76+104. doi: 10.3969/j.issn.1674-2230.2021.03.015 WANG Z X, ZHANG Y P, ZHAN Y F. The soft-kill distributed coordination layer defense system technology for the low-slow-small drone[J]. Electronic Information Warfare Technology, 2021, 36(3): 72-76+104. (in Chiness) doi: 10.3969/j.issn.1674-2230.2021.03.015
[2]	DURRANT H, ROY N, ABBEEL P. Unmanned aircraft collision avoidance using continuous-state POMDPs[C]. Robotics: Science and Systems VⅡ, Los Angeles, CA, USA: University of Southern California, 2011.
[3]	TYAGI A, ZHANG Y, TOUSSAINT S, et al. Strategies to model system risk using UAS safety analysis model (USAM)[C]. 16^th AIAA Aviation Technology, Integration, and Operations Conference, Dallas, TX, USA: AIAA, 2015.
[4]	LANCOVS D, DMITRIJS H. Building, verifying and validating a collision avoidance model for unmanned aerial vehicles[J]. Procedia Engineering, 2017, 178(5): 155-161. http://www.sciencedirect.com/science/article/pii/S1877705817300826
[5]	ZHANG Z Y, ZHANG J, WANG P, et al. Research on operation of UAVs in non-isolated airspace[J]. Computers, Materials & Continua, 2018, 57(1): 151-166. http://www.zhangqiaokeyan.com/academic-journal-foreign_other_thesis/0204112535702.html
[6]	GAN X, WU Y, LIU P, et al. Dynamic collision avoidance zone modeling method based on UAV emergency collision avoidance trajectory[C]. 2020 IEEE International Conference on Artificial Intelligence and Information Systems(ICAⅡS), Fukuoka, Japan: IEEE, 2020.
[7]	刘畅, 王宏伦, 姚鹏, 等. 面向空中威胁的无人机动态碰撞区建模与分析[J]. 北京航空航天大学学报, 2015, 41(7): 1231-1238. https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201507012.htm LIU C, WANG H L, YAO P, et al. Modeling and analysis of dynamic collision region for UAV avoiding aerial intruders[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(7): 1231-1238. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201507012.htm
[8]	高俊杰. 无人机安全飞行风险评估研究[D]. 四川广汉: 中国民用航空飞行学院, 2018. GAO J J. Safety flight risk assessment study of unmanned aircraft[D]. Guanghan, Sichuan: Civil Aviation Flight University of China, 2018. (in Chiness)
[9]	贺强, 徐艺, 马尧. 基于FCM的无人机飞行安全风险评估[J]. 民航学报, 2018, 2(1): 31-34+21. https://www.cnki.com.cn/Article/CJFDTOTAL-MHXE201801009.htm HE Q, XU Y, MA Y. UAS flight safety risk assessment based on fuzzy cognitive maps[J]. Journal Of Civil Aviation, 2018, 2 (1): 31-34+21. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-MHXE201801009.htm
[10]	杨新湦, 任治. 基于尾涡强度的无人机与民机纵向安全间隔评估研究[J]. 中国安全生产科学技术, 2018, 14(10): 64-69. https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201810010.htm YANG X S, REN Z. Research on assessment of longitudinal safety separation between UAV and civil aircraft based on trailing vortex intensity[J]. Journal of Safety Science and Technology, 2018, 14(10): 64-69. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201810010.htm
[11]	邓力. 无人机与民航客机碰撞概率研究[J]. 南京理工大学学报, 2019, 43(1): 122-128. https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201901017.htm DENG L. Research of collision probability of unmanned aerial vehicles and civil airplane[J]. Journal of Nanjing University of Science and Technology, 2019, 43(1): 122-128. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201901017.htm
[12]	潘卫军, 陈佳炀, 张智巍, 等. 管制空域内无人机与有人机侧向碰撞风险研究[J]. 计算机与现代化, 2020(3): 1-5. https://www.cnki.com.cn/Article/CJFDTOTAL-JYXH202003003.htm PAN W J, CHEN J Y, ZHANG Z W, et al. Lateral collision risk evaluation between unmanned aerial vehicle and manned aircraft in controlled airspace[J]. Computer and Modernization. 2020(3): 1-5. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-JYXH202003003.htm
[13]	赵建华, 王华伟, 严晓婧, 等. 无人机与运输类飞机碰撞风险研究[J]. 舰船电子工程, 2021, 41(1): 26-29+76. doi: 10.3969/j.issn.1672-9730.2021.01.007 ZHAO J H, WANG H W, YAN X J, et al. Research on collision risk between drones and transport aircraft[J]. Ship Electronic Engineering 2021, 41(1): 26-29+76. (in Chiness) doi: 10.3969/j.issn.1672-9730.2021.01.007
[14]	王莉莉, 阳杰. 基于位置误差概率模型的物流无人机安全间隔评估方法研究[J]. 中国安全生产科学技术, 2022, 18 (3): 184-192. https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202203028.htm WANG L L, YANG J. Research on assessment method of safety separation for logistics UAVs based on position error probability model[J]. Journal of Safety Science and Technology, 2022, 18(3): 184-192. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202203028.htm
[15]	高扬, 刘单单. 低空开放后终端区航空器碰撞风险模型研究[J]. 中国安全科学报, 2014, 24(6): 141-145. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201406024.htm GAO Y, LIU D D. Research on model for aircrafts collision risk in terminal area after low altitude open[J]. China Safety Science Journla, 2014, 24(6): 141-145. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201406024.htm
[16]	赵建华. 考虑无人机运行的航空安全风险分析研究[D]. 南京: 南京航空航天大学, 2020. ZHAO J H. Risk analysis of aviation safety considering UAV operation[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020. (in Chiness)
[17]	姜黎黎, 王保国, 刘淑艳, 等. 人的可靠性研究中的定量分析方法及其评价[C]. 人-机-环境系统工程研究进展. 北京: 中国系统工程学会, 2005. JIANG L L, WANG B G, LIU S Y, et al. Quantitative analysis method and its evaluation in human reliability research[C]. Progress in Human-Machine-Environmental Systems Engineering, Beijing: Systems Engineering Society of China, 2005. (in Chiness)
[18]	钟若嵋, 文小航, 徐晨晨. 基于高分辨率模式的京津冀地区无人机航路风向风速模拟分析[J]. 地理科学进展, 2021, 40 (9): 1528-1539. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ202109009.htm ZHONG R M, WEN X H, XU C C. Simulation and analysis of wind speed and direction of unmanned aerial vehicle route in the Beijing-Tianjin-Hebei region based on high resolution model[J]. Progress in Geography, 2021, 40(9): 1528-1539. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ202109009.htm
[19]	张宏宏, 甘旭升, 李昂, 等. 基于速度障碍法的无人机避障与航迹恢复策略[J]. 系统工程与电子技术, 2020, 42(8): 1759-1767. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD202008017.htm ZHANG H H, GAN X S, LI A, et al. UAV obstacle avoidance and track recover strategy based on velocity obstacle method[J]. Systems Engineering and Electronics, 2020, 42 (8): 1759-1767. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD202008017.htm
[20]	史校川, 金镭, 王春生, 等. 美国军民用无人机系统事故案例分析[J]. 航空标准化与质量, 2017(3): 46-49. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBZ201703012.htm SHI X C, JIN L, WANG C S, et al. A case study of the accidents of UAVs used by The United States[J]. Aeronautic Standardization & Quality, 2017(3): 46-49. (in Chiness) https://www.cnki.com.cn/Article/CJFDTOTAL-HKBZ201703012.htm