考虑低碳及速度调整偏差的航空器集群改进速度障碍法

doi:10.16265/j.cnki.issn1003-3033.2026.04.0144

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (4): 142-151.doi: 10.16265/j.cnki.issn1003-3033.2026.04.0144

考虑低碳及速度调整偏差的航空器集群改进速度障碍法

钟庆伟¹^,²(), 庾映雪¹^,³, 刘苏¹^,^**(), 王睿¹, 郭经纬⁴, 潘卫军²

¹ 中国民用航空飞行学院空中交通管理学院, 四川广汉 618307
² 中国民用航空飞行学院民航飞行技术与飞行安全重点实验室, 四川广汉 618307
³ 广州城建职业学院机电工程学院, 广东广州 510925
⁴ 澳门城市大学商学院, 中国澳门 999078

收稿日期:2025-11-02 修回日期:2026-01-10 出版日期:2026-04-28
通信作者:
**刘苏(1988—),女,重庆人,博士,讲师,主要从事交通运输系统仿真与决策智能化研究。E-mail:cdliusu@my.swjtu.edu.cn。
作者简介:
钟庆伟 (1991—),男,四川什邡人,博士,副教授,主要从事交通运输组织优化方面的研究。E-mail:qingweizhong@cafuc.edu.cn。
王睿, 讲师
郭经纬, 副教授
潘卫军, 教授
基金资助:
国家自然科学基金资助(U233209); 民航飞行技术与飞行安全重点实验室开放基金资助(F2024KF07C); 四川省民航飞行技术与飞行安全工程技术研究中心项目(GY2025-25D); 中央高校基本科研专项资助项目(25CAFUC09018)

Aircraft swarm improved velocity obstacle methods considering low carbon and deviation

Zhong Qingwei¹^,²(), Yu Yingxue¹^,³, Liu Su¹^,^**(), Wang Rui¹, Guo Jingwei⁴, Pan Weijun²

¹ School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
² Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan Sichuan, 618307, China
³ School of Electromechanical Engineering, Guangzhou City Construction College, Guangzhou Guangdong 510925, China
⁴ Faculty of Business, City University of Macau, Macau 999078, China

Received:2025-11-02 Revised:2026-01-10 Published:2026-04-28

摘要/Abstract

摘要：

为满足民航运行中低碳减排与冲突解脱安全性并重的需求,在考虑航空器碳排放及速度调整偏差的基础上,提出基于自适应椭圆保护区的改进速度障碍法(IVO)。首先,根据航空器速度差异构造自适应椭圆保护区;其次,将同一航路、矢量速度相似、距离接近的航空器划分为集群,并刻画集群的质点、速度矢量和安全范围等特征;再次,针对已划分的集群,采用集群控制算法合理调整集群内航空器的速度矢量,确保冲突解脱期间保持安全间隔;然后,在集群间建立速度障碍锥,结合单位时间最大改变量限制,通过平面几何分析与数学优化模型求解低碳目标下偏差最小的解脱速度和航向;最后,通过Python基于数值模拟实现动态行为分析。研究结果表明:与传统速度障碍法相比,改进方法使航空器解脱效率提高87.5%,平均调整幅度优化86.67%,燃油节省最高达37.36%,为复杂空中交通环境下冲突解脱与运行优化问题提供了一种可行的技术方案。

关键词: 低碳, 速度调整偏差, 航空器集群, 改进速度障碍法(IVO), 冲突解脱

Abstract:

In order to address the requirements of low-carbon operation and safety in aircraft conflict resolution, an IVO method based on adaptive elliptical protected zones was proposed, taking into account aircraft carbon emissions and speed adjustment deviations. First, adaptive elliptical protected zones were constructed according to the velocity differences among aircraft. Second, aircraft flying on the same route with similar velocity vectors and close spatial proximity were grouped into clusters, and key characteristics such as cluster centroids, velocity vectors, and safety regions were defined. Then, a cluster control algorithm was applied to reasonably adjust the velocity vectors of aircraft within each cluster, ensuring the maintenance of safe separation during conflict resolution. Subsequently, velocity obstacle cones were established between clusters. By incorporating constraints on the maximum allowable adjustment per unit time and combining planar geometric analysis with a mathematical optimization model, conflict-free velocities and headings with minimal deviation were determined under low-carbon objectives. Finally, dynamic behaviour analysis was conducted through numerical simulations implemented in Python. The results show that, compared with the traditional velocity obstacle method, the proposed approach improves conflict resolution efficiency by 87.5%, reduces the average adjustment magnitude by 86.67%, and achieves fuel savings of up to 37.36%, demonstrating its effectiveness for aircraft conflict resolution and operational optimization in complex air traffic environments.

Key words: low carbon, velocity adjustment deviation, aircraft swarm, improved velocity obstacle(IVO) method, aircraft conflict resolution

中图分类号:

X949

钟庆伟, 庾映雪, 刘苏, 王睿, 郭经纬, 潘卫军. 考虑低碳及速度调整偏差的航空器集群改进速度障碍法[J]. 中国安全科学学报, 2026, 36(4): 142-151.

Zhong Qingwei, Yu Yingxue, Liu Su, Wang Rui, Guo Jingwei, Pan Weijun. Aircraft swarm improved velocity obstacle methods considering low carbon and deviation[J]. China Safety Science Journal, 2026, 36(4): 142-151.

图/表 16

图1

图2

图3

图4

图5

图6

表1

表2

表3

图7

图8

表4

图9

图10

表5

图11

参考文献 18

[1]	王颖. “双碳”背景下数据中心绿色低碳发展对策[J]. 上海节能, 2024(5):780-783.
	Wang Ying. Countermeasures for green and low carbon development of data centers under the background of "Double Carbon"[J]. Shanghai Energy Conservation, 2024(5):780-783.
[2]	耿鹏, 杨豪杰, 薛芳琳, 等. 基于多无人机协同的林火安全探测及人员疏散[J]. 中国安全科学学报, 2025, 35(4):43-50. doi: 10.16265/j.cnki.issn1003-3033.2025.04.0958
	Geng Peng, Yang Haojie, Xue Fanglin, et al. Forest fire safety detection and personnel evacuation based on collaborative MUAVs[J]. China Safety Science Journal, 2025, 35(4):43-50. doi: 10.16265/j.cnki.issn1003-3033.2025.04.0958
[3]	周元炜, 兰翔. 2026年我国民航旅客运输量预测:基于运力约束与客源重构下的研判[J]. 空运商务, 2025(12):8-12.
	Zhou Yuanwei, Lan Xiang. Forecast of China's civil aviation passenger transport volume in 2026:an analysis based on capacity constraints and passenger source restructuring[J]. Air Transport Business, 2025(12): 8-12.
[4]	Fiorini P, Shiller Z. Motion planning in dynamic environments using the relative velocity paradigm[C]. Proceedings IEEE International Conference on Robotics and Automation. IEEE, 1993: 560-565.
[5]	Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles[J]. The International Journal of Robotics Research, 1998, 17(7):760-772. doi: 10.1177/027836499801700706
[6]	Wilkie D, Van D B J, Manocha D. Generalized velocity obstacles[C]. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2009:5573-5578.
[7]	Van D B J, Lin Ming, Manocha D. Reciprocal velocity obstacles for real-time multi-agent navigation[C]. 2008 IEEE International Conference on Robotics and Automation, 2008:1928-1935.
[8]	Yu Yingxue, Ai Yi, Zhong Qingwei, et al. A refined protected zone model and conflict-aware algorithm for manned and unmanned fusion airspace[J]. International Journal of Aeronautical and Space Sciences, 2025, 26:1427-1451. doi: 10.1007/s42405-024-00851-0
[9]	Du Zhe, Li Weifeng, Shi Guoyou. Multi-USV collaborative obstacle avoidance based on improved velocity obstacle method[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2024, 10(1): DOI: 10.1061/AJRUA6.RUENG-1174.
[10]	Pan Weijun, Qin Liru, He Qinyue, et al. Three-dimensional flight conflict detection and resolution based on particle swarm optimization[J]. Aerospace, 2023, 10(9): DOI: 10.3390/aerospace10090740.
[11]	Gao Jing, Zhang Hongtao, Tan Li, et al. UAV dynamic obstacle avoidance based on improved reciprocal velocity obstacle[J]. Journal of Physics: Conference Series, 2022, 2216(1): DOI: 10.1088/1742-6596/2216/1/012014.
[12]	Zhu Hongyang, Ding Yi. Optimized dynamic collision avoidance algorithm for USV path planning[J]. Sensors, 2023, 23(9): DOI: 10.3390/s23094567.
[13]	Zheng Huarong, Zhu Jiangbo, Liu Chenguang, et al. Regulation aware dynamic path planning for intelligent ships with uncertain velocity obstacles[J]. Ocean Engineering, 2023,278: DOI: 10.1016/j.oceaneng.2023.114401.
[14]	Peng Mingzhu, Meng Wei. Cooperative obstacle avoidance for multiple UAVs using spline_VO method[J]. Sensors, 2022, 22 (5): DOI: 10.3390/s22051947.
[15]	Xue Delai, Wu Defeng, Yamashita A S, et al. Proximal policy optimization with reciprocal velocity obstacle based collision avoidance path planning for multi-unmanned surface vehicles[J]. Ocean Engineering, 2023,273: DOI: 10.1016/j.oceaneng.2023.114005.
[16]	彭娅婷, 温祥西, 吴明功, 等. TBO模式下基于复杂网络的空中交通复杂性分析[J]. 北京航空航天大学学报, 2025, 51(4):1234-1244.
	Peng Yating, Wen Xiangxi, Wu Minggong, et al. Complex network-based air traffic complexity analysis in TBO[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(4):1234-1244.
[17]	王红勇, 邓涛涛, 徐文强. 基于调速的飞行冲突探测与解脱方法[J]. 科学技术与工程, 2021, 21(13): 5584-5591.
	Wang Hongyong, Deng Taotao, Xu Wenqiang. Conflict detection and resolved method based on speed regulation[J]. Science, Technology and Engineering, 2021, 21(13):5584-5591.
[18]	刘钊, 罗辰汉, 张明阳. 多碍航物水域救援船路径规划方法[J]. 中国安全科学学报, 2023, 33(7): 90-97. doi: 10.16265/j.cnki.issn1003-3033.2023.07.2046
	Liu Zhao, Luo Chenhan, Zhang Mingyang. Path planning method of rescue ships in waters with multiple obstacles[J]. China Safety Science Journal, 2023, 33(7): 90-97. doi: 10.16265/j.cnki.issn1003-3033.2023.07.2046

编号	机型	(x_i, y_i)/ km	a_i/ km	b_i/ km	θ_i/ (°)	v_i/ (km/min)	机型	(x_j, y_j)/ km	a_j/ km	b_j/ km	θ_j/ (°)	v_j/ (km/min)
1	A320	(60,60)	1.65	1.47	50	5.5	DA40	(50,90)	1.35	1.03	5	4.5
1	A320	(60,60)	10.00	10.00	50	5.5	DA40	(50,90)	10.00	10.00	5	4.5
2	CY-09	(200,330)	0.65	0.03	120	2.17	C-172	(215,350)	1.14	0.73	200	3.8
2	CY-09	(200,330)	7.00	7.00	120	2.17	C-172	(215,350)	7.00	7.00	200	3.8
3	CY-12	(60,70)	0.80	0.24	20	2.67	HC-540	(80,100)	0.72	0.13	280	2.4
3	CY-12	(60,70)	7.00	7.00	20	2.67	HC-540	(80,100)	7.00	7.00	280	2.4

编号	$\left\|v{\text{'}}_{i}\right\|$/ (km/min)	$\left\|v{″}_{i}\right\|$/ (km/min)	θ'_i/ (°)	θ″_i/ (°)	min$\left\|v{\text{'}}_{i}\right\|$ 调整量	max$\left\|v{″}_{i}\right\|$ 调整量	minθ″_i 调整量	maxθ″_i 调整量
1	4.75	5.58	48.89	63.03	0.08	0.75	1.11	13.03
1	2.75	12.32	21.76	117.92	2.75	6.82	28.24	67.92
2	2.06	2.48	113.47	167.64	0.11	0.31	6.53	47.64
2	—	6.46	—	17.45	—	4.29	—	102.55
3	2.61	3.02	14.70	21.00	0.06	0.35	1.00	5.30
3	2.03	6.56	-13.32	32.05	0.64	3.89	12.05	33.32

编号	(x_i, y_i)/km	a_i/km	b_i/km	θ_i/(°)	v_i/(km/min)
1	(60,73)	1.65	1.47	2	5.4
2	(20,78)	1.65	1.47	0	5.2
3	(80,58)	1.35	1.03	80	4.5
4	(82,55)	1.35	1.03	83	4.4
5	(50,80)	1.14	0.73	5	3.8
6	(45,73)	1.14	0.73	6	3.7
7	(20,70)	0.72	0.13	10	2.4
8	(85,75)	0.72	0.13	10	2.4
9	(40,50)	0.8	0.24	60	2.6
10	(65,73)	1.65	1.47	4	4.9

集群	航空器	角度差/(°)	速度差/(km/min)
m₅	p₁	0.95	0.25
m₅	p₁₀	1.05	0.25
m₆	p₃	1.48	0.05
m₆	p₄	1.52	0.05

考虑低碳及速度调整偏差的航空器集群改进速度障碍法

Aircraft swarm improved velocity obstacle methods considering low carbon and deviation

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 18

相关文章 2

编辑推荐

Metrics

本文评价

[1]	焦卫东, 姚军强, 王瑞冬. 基于凸包围盒的飞行冲突检测算法^*[J]. 中国安全科学学报, 2021, 31(12): 32-38.
[2]	李启平. 低碳农业对农产品安全的影响研究[J]. 中国安全科学学报, 2010, 20(3): 145-.