基于速度障碍法的无人机避障与航迹恢复策略

doi:10.3969/j.issn.1001-506X.2020.08.16

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (8): 1759-1767.doi: 10.3969/j.issn.1001-506X.2020.08.16

基于速度障碍法的无人机避障与航迹恢复策略

张宏宏^1,²(), 甘旭升^1,^2,*(), 李昂^1,²(), 高志强³(), 徐鑫宇¹()

1. 空军工程大学空管领航学院, 陕西西安 710051
2. 空管防相撞技术国家重点实验室, 陕西西安 710051
3. 空军工程大学基础部, 陕西西安 710051

收稿日期:2020-01-03 出版日期:2020-07-25 发布日期:2020-07-27
通讯作者: 甘旭升 E-mail:anhuifuyangzhh@sina.com;gxsh15934896556@qq.com;18251826766@163.com;1785719531@qq.com;2211070853@163.com
作者简介:张宏宏 (1995-),男,硕士研究生,主要研究方向为飞行冲突探测与解脱。E-mail:anhuifuyangzhh@sina.com|李昂 (1996-),男,硕士研究生,主要研究方向为空中交通相依网络。E-mail:18251826766@163.com|高志强 (1996-),男,硕士研究生,主要研究方向为信息处理技术。E-mail:1785719531@qq.com|徐鑫宇 (1996-),男,硕士研究生,主要研究方向为空域管理。E-mail:2211070853@163.com
基金资助:
国家自然科学基金(61601497)

UAV obstacle avoidance and track recovery strategy based onvelocity obstacle method

Honghong ZHANG^1,²(), Xusheng GAN^1,^2,*(), Ang LI^1,²(), Zhiqiang GAO³(), Xinyu XU¹()

1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China
2. National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
3. Department of Basic Sciences, Air Force Engineering University, Xi'an 710051, China

Received:2020-01-03 Online:2020-07-25 Published:2020-07-27
Contact: Xusheng GAN E-mail:anhuifuyangzhh@sina.com;gxsh15934896556@qq.com;18251826766@163.com;1785719531@qq.com;2211070853@163.com
Supported by:
国家自然科学基金(61601497)

摘要/Abstract

摘要：

针对在融合空域内,面临复杂障碍物时无人机飞行冲突解脱和航迹恢复问题,在速度障碍法的基础上提出了一种几何优化方法,并对这个问题进行了严格的数学描述。首先，根据无人机与障碍物之间的相对位置和速度关系,基于航迹预推模型确定其冲突类型,以及是否满足各解脱策略的条件。然后，选取相应的解脱策略。待冲突解脱完成后,无人机恢复至原飞行航线。之后，通过几何分析与理论推导证明该模型能够有效解决飞行冲突,并且给出了具体的冲突解脱和航迹恢复的时间。最后，在仿真中,所提方法根据不同场景自主选择冲突解脱策略,结果显示该方法简单高效。

关键词: 速度障碍, 冲突探测, 冲突解脱, 航迹恢复, 几何优化算法

Abstract:

Aiming at the problem of flight conflict resolution and track recovery of unmanned aerial vehicle (UAV) with complex obstacles in the fusion airspace, a geometric optimization method based on the velocity obstacle method is proposed, and the problem is described in strict mathematics. Firstly, according to the relative position and speed relationship between the UAV and the obstacle, the conflict type is determined based on the track pre-propelling model, and whether the conditions of each resolution strategy are met is also determined. Secondly, the corresponding resolution strategy is selected. After the resolution of the conflict is completed, the UAV will resume the original flight path. Then, geometric analysis and theoretical derivation prove that the model can effectively solve the flight conflict, and give the specific time of conflict resolution and track recovery. Finally, in the simulation, the proposed method chooses the conflict resolution strategy autonomously according to different scenaries, which shows that this method is simple and efficient.

Key words: speed obstacle, conflict detection, conflict resolution, track recovery, geometric optimization algorithm

中图分类号:

V249.1

张宏宏, 甘旭升, 李昂, 高志强, 徐鑫宇. 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.

Honghong ZHANG, Xusheng GAN, Ang LI, Zhiqiang GAO, Xinyu XU. UAV obstacle avoidance and track recovery strategy based onvelocity obstacle method[J]. Systems Engineering and Electronics, 2020, 42(8): 1759-1767.

图/表 18

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

图11

图12

图13

图14

表2

表3

表4

参考文献 28

1	SHIN H S, TSOURDOS A, WHITE B A, et al. UAV conflict detection and resolution for static and dynamic obstacles[C]//Proc.of the Guidance, Navigation and Control Conference and Exhibit, 2008: AIAA 2008-6521.
2	DU Y, NAN Y. Research of robot path planning based on improved artificial potential field[C]//Proc.of the International Conference on Advances in Mechanical Engineering and Industrial Informatics, 2016: 1025-1030.
3	ZHAO J, BIN Y, RUN Y. The flight navigation planning based on potential field ant colony algorithm[C]//Proc.of the International Conference on Advanced Control, Automation and Artificial Intelligence, 2018: 200-204.
4	ZHANG N, ZHANG Y, MA C, et al. Path planning of six-DOF serial robots based on improved Artificial potential field method[C]//Proc.of the IEEE International Conference on Robots and Biomimetics, 2017: 617-621.
5	OROZCO-ROSAS U , MONTIEL O , SEPULVEDA R . Mobile robot path planning using membrane evolutionary artificial field[J]. Applied Soft Computing, 2019, 77, 236- 251.
6	ZHANG W , GONG X , HAN G , et al. An improved ant colony algorithm for path planning in one scenic area with many pots[J]. IEEE Access, 2017, 5, 13260- 13269.
7	MAC T T , COPOT C , TRAN D T , et al. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]. Applied Soft Computing, 2017, 59, 68- 76.
8	YEN C T , CHENG M F . A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance[J]. Microsystem Technologies, 2018, 24 (1): 125- 135.
9	DARWISH A H , JOUKHADAR A , KASHKASH M . Using the bees algorithm for wheeled mobile robot path planning in an indoor dynamic environment[J]. Cogent Engineering, 2018, 5 (1): 1- 23.
10	PRANDINI M , HU J , LYGEROS J , et al. A probabilistic approach to aircraft conflict detection[J]. IEEE Trans.on Intelligent Transportation Systems, 2000, 1 (4): 199- 200.
11	聂俊岚, 张庆杰, 王艳芬. 基于加权Voronoi图的无人飞行器航迹规划[J]. 飞行力学, 2015, 33 (4): 339- 343.
	NIE J L , ZHANG Q J , WANG Y F . Flight path planning of uav based on weighted voronoi diagram[J]. Flight Dynamics, 2015, 33 (4): 339- 343.
12	BILLIMORIA K D. A geometric optimization approach to aircraft conflict resolution[C]//Proc.of the 18th Applied Aerodynamics Conference, 2000: AIAA 2000-4265.
13	BILLIMORIA K D , SRIDHAR B , CHATTERJI G B , et al. Facet: future ATM concepts evaluation tool[J]. Air Traffic Control Quarterly, 2001, 9 (1): 1- 20.
14	WHITE B A, SHIN H S, TSOURDOS A. UAV obstacle avoi- dance using differential geometry concepts[C]//Proc.of the 18th World Congress, International Federation of Automatic Control, 2011: 6325-6330.
15	GESER A, MUNOZ C. A geometric approach to strategic conflict detection and resolution[C]//Proc.of the Digital Avionics Systems Conference. Piscataway, 2002.
16	ZHANG Y, ZHANG M, YU J. Realtime flight conflict detection and release based on multi-agent system[C]//Proc.of the IOP Conference Series: Earth and Environmental Science, 2018: 032053.
17	FIORINI P . Motion planning in dynamic environments using velocity obstacles[J]. International Journal of Robotics Research, 1998, 17 (7): 760- 772.
18	Durand N , BARNIER N . Does ATM need centralized coordination? Autonomous conflict resolution analysis in a constrained speed environment[J]. Air Traffic Control Quarterly, 2015, 23 (4): 710- 712.
19	杨秀霞, 张毅, 周硙硙. 一种动态不确定环境下UAV自主避障算法[J]. 系统工程与电子技术, 2017, 39 (11): 2546- 2552.
	YANG X X , ZHANG Y , ZHOU W W . Automatic obstacle avoidance algorithm for UAV in dynamic uncertain environment[J]. Systems Engineering and Electronics, 2017, 39 (11): 2546- 2552.
20	王泽坤, 吴明功, 温祥西. 基于速度障碍法的飞行冲突解脱与恢复策略[J]. 北京航空航天大学学报, 2019, 45 (7): 1294- 1302.
	WANG Z K , WU M G , WEN X X , et al. Flight collision resolution and recovery strategy based on velocity obstacle method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (7): 1294- 1302.
21	魏建, 李相民, 代进进. 基于速度障碍法的无人机避免机动障碍物问题[J]. 火力与指挥控制, 2016, 41 (8): 84- 92.
	WEI J , LI X M , DAI J J . Avoidance of maneuvering obstacles in UAV based on speed obstacle method[J]. Firepower and command and control, 2016, 41 (8): 84- 92.
22	杨秀霞, 周硙硙, 张毅. 基于速度障碍圆弧法的UAV自主避障规划研究[J]. 系统工程与电子技术, 2017, 39 (11): 168- 176.
	YANG X X , ZHOU W W , ZHANG Y . Automatic obstacle avoi- dance planning for UAV based on velocity obstacle arc method[J]. Systems Engineering and Electronics, 2017, 39 (11): 168- 176.
23	XU Y J, XIN M, WANG J N. Unmanned aerial vehicle formation flight via a hierarchical cooperative control approach[C]//Proc.of the AIAA Guidance, Navigation, and Control Confe-rence, 2011: 500-507.
24	DURAND N . Constant speed optimal reciprocal collision avoida-nce[J]. Transportation Research Part C: Emerging Technologies, 2018, 96, 366- 379.
25	全权, 李刚, 柏艺琴, 等. 低空无人机交通管理概览与建议[J]. 航空学报, 2020, 41 (1): 023238.
	QUAN Q , LI G , BAI Y Q , et al. Low altitude UAV traffic management: An introductory overview and proposal[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41 (1): 023238.
26	蒋旭瑞, 吴明功, 温祥西, 等. 基于合作博弈的多机飞行冲突解脱策略[J]. 系统工程与电子技术, 2018, 40 (11): 2482- 2489.
	JIANG X R , WU M G , WEN X X , et al. Multi-aircraft conflict resolution strategy based on cooperative game[J]. Systems engineering and electronic technology, 2018, 40 (11): 2482- 2489.
27	张玮, 马焱, 赵捍东, 等. 基于改进烟花-蚁群混合算法的智能移动体避障路径规划[J]. 控制与决策, 2019, 34 (2): 335- 343.
	ZHANG W , MA Y , ZHAO H D , et al. Intelligent moving body obstacle avoidance path planning based on the improved fireworks and ant colony hybrid algorithm[J]. Control and decision, 2019, 34 (2): 335- 343.
28	任彦, 赵海波, 肖永健. 改进势场蚁群法的机器人避障及路径规划[J]. 电光与控制, 2019, 26 (11): 75- 79.
	REN Y , ZHAO H B , XIAO Y J . Robot obstacle avoidance and path planning based on improved potential field ant colony method[J]. Electronics Optics & Control, 2019, 26 (11): 75- 79.

场景	类型	起点/km	航向/(°)	速度/(m/s)	仿真步长/h
1	无人机	(0, 0)	45	50	0.001
1	障碍物	(30, 30)	225	40	0.001
2	无人机	(0, 15)	0	50	0.001
2	障碍物	(15, 0)	90	50	0.001
3	无人机	(0, 0)	30	60	0.000 1
3	障碍物	(20, 0)	150	50	0.000 1

场景	类型	解脱坐标/km	最大航向改变量/(°)	解脱速度/(m/s)	判断	解脱方式
1	无人机	(12.86, 12.86)	42.8	50	$\Delta \theta \in\left[-\frac{\pi}{3}, \frac{\pi}{3}\right] $	航向解脱
1	障碍物	(19.72, 19.72)	0	40		航向解脱
2	无人机	(8.1, 15)	62.7	76.5	$\Delta \theta \notin\left[-\frac{\pi}{3}, \frac{\pi}{3}\right], v_{1}^{\prime} \in[\underline{v}, \bar{v}] $	速度解脱
2	障碍物	(15, 8.1)	0	50		速度解脱
3	无人机	(5.46, 3.15)	19.6	60	-	混合调配
	障碍物	(15.45, 2.63)	0	50		混合调配
	无人机	(5.46, 3.15)	59.3	40		混合调配
	障碍物	(15.45, 2.63)	0	50		混合调配

场景	求解时间/s	解脱时间/s	飞行距离(解脱前)/km	飞行距离(解脱后)/km	绕飞距离/km	绕飞距离占比/%	机动次数
1	0.01	2.69	10.20	10.98	0.78	7.6	2次
2	0.009	1.82	9.8	9.8	0	0	2次
3	0.009	1.45	13.04	13.24	0.20	1.5	2次
3	0.008	1.55	13.52	15.44	1.92	12.4	2次

指标	文献[26]	文献[27]	文献[28]	本文算法
求解时间/s	3.13	0.83	17.7	约0.01
机动次数	两次	多次	多次	两次
可操作性	强	弱	弱	强

[1]	毕可心, 吴明功, 张文斌, 温祥西, 杜坎. 基于速度障碍法的飞行冲突网络建模与分析[J]. 系统工程与电子技术, 2021, 43(8): 2163-2173.
[2]	吴明功, 王泽坤, 温祥西, 蒋旭瑞, 孙千锐. 飞行冲突解脱的几何优化模型[J]. 系统工程与电子技术, 2019, 41(4): 863-869.
[3]	蒋旭瑞, 吴明功, 温祥西, 涂从良, 聂党民. 基于合作博弈的多机飞行冲突解脱策略[J]. 系统工程与电子技术, 2018, 40(11): 2482-.
[4]	杨秀霞, 张毅, 周硙硙. 一种动态不确定环境下UAV自主避障算法[J]. 系统工程与电子技术, 2017, 39(11): 2546-2552.
[5]	杨秀霞, 周硙硙, 张毅. 基于速度障碍圆弧法的UAV自主避障规划研究[J]. 系统工程与电子技术, 2017, 39(1): 168-176.
[6]	张毅, 杨秀霞, 周硙硙. 基于速度障碍法的多UAV可飞行航迹优化生成[J]. 系统工程与电子技术, 2015, 37(2): 323-330.
[7]	郝大鹏，傅卫平，王雯. 基于行为动力学的移动机器人安全导航方法[J]. 系统工程与电子技术, 2014, 36(1): 136-142.

基于速度障碍法的无人机避障与航迹恢复策略

UAV obstacle avoidance and track recovery strategy based onvelocity obstacle method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 18

参考文献 28

相关文章 7

编辑推荐

Metrics

本文评价