基于动态障碍物可达包络分析的无人机编队路径规划

doi:10.12305/j.issn.1001-506X.2025.04.24

摘要/Abstract

摘要：

针对无人机编队对行为未知动态障碍物的规避问题, 提出一种基于动态障碍物可达包络分析的无人机编队路径规划方法。首先, 建立无人机编队运动模型, 并利用领航-跟随法维持期望的编队构形。其次, 考虑到动态障碍物运动状态的不确定性, 建立含有不确定因素的非线性运动模型, 并通过状态转移张量法得到不确定因素的协方差传递方程, 进而计算出预测时域内障碍物的可达包络。最后, 对障碍物可达包络建立势场模型, 利用人工势场法实现无人机编队避障飞行。对比仿真实验结果表明, 所提方法能够在线一次性给出行为未知动态障碍物的可达包络, 并有效解决无人机编队在避开动态障碍物时出现的路径绕远等问题。

关键词: 无人机编队, 动态障碍物, 不确定性, 状态转移张量法, 可达包络分析

Abstract:

To solve the problem of unmanned aerial vehicle (UAV) formation to avoid dynamic obstacles with unknown behaviors, a UAVs formation path planning method based on the reachable envelope analysis of dynamic obstacles is proposed. Firstly, the model of the UAVs formation motion is established and the desired formation configuration is formed using the leader-follower method. Secondly, considering the uncertainty of the motion dynamic states of dynamic obstacles, a nonlinear motion model containing uncertainties is established, and the covariance propagation equation of the uncertainties is obtained by the state transfer tensor method, which generates the reachable envelope of the obstacle in the predicted time domain. Finally, the potential field model is established for the reachable envelope of the obstacle, and the artificial potential field method is applied to realize the UAVs formation obstacle avoidance flight. The results of comparative simulation experiments show that the proposed method can generate the reachable envelope of dynamic obstacles with unknown behaviors at one time online, and effectively solve problems such as path bypassing when the UAV formation avoids dynamic obstacles.

Key words: unmanned aerial vehicle (UAV) formation, dynamic obstacle, uncertainty, state transfer tensor method, reachable envelope analysis

中图分类号:

殷泽阳, 梁浩, 廖宇新, 陈晓方, 谢永芳. 基于动态障碍物可达包络分析的无人机编队路径规划[J]. 系统工程与电子技术, 2025, 47(4): 1275-1284.

Zeyang YIN, Hao LIANG, Yuxin LIAO, Xiaofang CHEN, Yongfang XIE. UAV formation path planning based on reachable envelope analysis of dynamic obstacle[J]. Systems Engineering and Electronics, 2025, 47(4): 1275-1284.

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参考文献 33

1	WU T , WANG J , TIAN B L . Periodic event-triggered formation control for multi-UAV systems with collision avoidance[J]. Chinese Journal of Aeronautics, 2022, 35 (8): 193- 203. doi: 10.1016/j.cja.2021.10.011
2	DE-MORAES R S , DE-FREITAS E P . Multi-UAV based crowd monitoring system[J]. IEEE Trans. on Aerospace and Electronic Systems, 2019, 56 (2): 1332- 1345.
3	WEI C S , LUO J J , YIN Z Y , et al. Leader-following consensus of second-order multi-agent systems with arbitrarily appointed-time prescribed performance[J]. IET Control Theory & Applications, 2018, 12 (16): 2276- 2286.
4	DOKUYUCU HI , OZMEN N G . A non-contact object delivery system using leader-follower formation control for multi-robots[J]. International Journal of Applied Methods in Electronics and Computers, 2023, 11 (3): 134- 144.
5	LI Y D , ZHU L , GUO Y . Observer-based multivariable fixed-time formation control of mobile robots[J]. Journal of Systems Engineering and Electronics, 2020, 31 (2): 403- 414. doi: 10.23919/JSEE.2020.000017
6	YAN X , JIANG D P , MIAO R L , et al. Formation control and obstacle avoidance algorithm of a multi-USV system based on virtual structure and artificial potential field[J]. Journal of Marine Science and Engineering, 2021, 9 (2): 161- 177. doi: 10.3390/jmse9020161
7	YAN T , XU Z , YANG S X . Consensus formation tracking for multiple UAV systems using distributed bioinspired sliding mode control[J]. IEEE Trans.on Intelligent Vehicles, 2022, 8 (2): 1081- 1092.
8	YANG Y , XIAO Y , LI T S . A survey of autonomous underwater vehicle formation: performance, formation control, and communication capability[J]. IEEE Communications Surveys & Tutorials, 2021, 23 (2): 815- 841.
9	LI S , FANG X . A modified adaptive formation of UAV swarm by pigeon flock behavior within local visual field[J]. Aerospace Science and Technology, 2021, 114, 106736. doi: 10.1016/j.ast.2021.106736
10	BALCH T , ARKIN R C . Behavior-based formation control for multirobot teams[J]. IEEE Trans.on Robotics and Automation, 1998, 14 (6): 926- 939. doi: 10.1109/70.736776
11	HU J Q , WU H S , ZHAN R J , et al. Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior[J]. Journal of Systems Engineering and Electro-nics, 2021, 32 (6): 1463- 1476. doi: 10.23919/JSEE.2021.000124
12	CHEN L , DUAN H B . Collision-free formation-containment control for a group of UAVs with unknown disturbances[J]. Aerospace Science and Technology, 2022, 126, 107618. doi: 10.1016/j.ast.2022.107618
13	DOU L Q , CAI S Y , ZHANG X Y , et al. Event-triggered-based adaptive dynamic programming for distributed formation control of multi-UAV[J]. Journal of the Franklin Institute, 2022, 359 (8): 3671- 3691. doi: 10.1016/j.jfranklin.2022.02.034
14	徐星光, 王晓峰, 姚璐, 等. 固定翼无人机编队构型与通信拓扑优化[J]. 系统工程与电子技术, 2022, 44 (9): 2936- 2946. doi: 10.12305/j.issn.1001-506X.2022.09.29
	XU X G , WANG X F , YAO L , et al. Formation configuration a nd communication topology optimization for fixed-wing UAVs[J]. Systems Engineering and Electronics, 2022, 44 (9): 2936- 2946. doi: 10.12305/j.issn.1001-506X.2022.09.29
15	吴立尧, 苏析超, 王垒, 等. 有人/无人机编队队形集结控制研究[J]. 系统工程与电子技术, 2023, 45 (7): 2192- 2202.
	WU L Y , SU X C , WANG L , et al. Research of formation rendezvous control for manned/ unmanned aerial vehicles for mation[J]. Systems Engineering and Electronics, 2023, 45 (7): 2192- 2202.
16	SEO J , KIM Y , KIM S , et al. Collision avoidance strategies for unmanned aerial vehicles in formation flight[J]. IEEE Trans.on Aerospace and Electronic Systems, 23017, 56 (6): 2718- 2734.
17	WANG N , DAI J Y , YING J . UAV formation obstacle avoidance control algorithm based on improved artificial potential field and consensus[J]. International Journal of Aeronautical and Space Sciences, 2021, 22 (6): 1413- 1427. doi: 10.1007/s42405-021-00407-6
18	陈锦涛, 李鸿一, 任鸿儒, 等. 基于RRT森林算法的高层消防多无人机室内协同路径规划[J]. 自动化学报, 2023, 49 (12): 2615- 2626.
	CHEN J T , LI H Y , REN H R , et al. Cooperative indoor path planning of multi-UAVs for high-rise fire fighting based on RRT-forest algorithm[J]. Acta Automatica Sinica, 2023, 49 (12): 2615- 2626.
19	WU Y , GOU J Z , HU X T , et al. A new consensus theory-based method for formation control and obstacle avoidance of UAVs[J]. Aerospace Science and Technology, 2020, 107, 106332. doi: 10.1016/j.ast.2020.106332
20	费思远, 鲜斌, 王岭. 基于群集行为的分布式多无人机编队动态避障控制[J]. 控制理论与应用, 2022, 39 (1): 1- 11.
	FEI S Y , XIAN B , WANG L . Distributed formation control for multiple unmanned aerial vehicles with dynamic obstacle avoidance based on the flocking behavior[J]. Control Theory and Technology, 2022, 39 (1): 1- 11.
21	LINDQVIST B , MANSOURI S S , AGHA-MOHAMMADI A , et al. Nonlinear MPC for collision avoidance and control of UAVs with dynamic obstacles[J]. IEEE Robotics and Automation Letters, 2020, 5 (4): 6001- 6008. doi: 10.1109/LRA.2020.3010730
22	SHIN J , KIM H J . Nonlinear model predictive formation flight[J]. IEEE Trans.on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2009, 39 (5): 1116- 1125. doi: 10.1109/TSMCA.2009.2021935
23	LIN W . Distributed UAV formation control using differential game approach[J]. Aerospace Science and Technology, 2014, 35, 54- 62. doi: 10.1016/j.ast.2014.02.004
24	LIANG Y Q , DONG Q , ZHAO Y J . Adaptive leader-follower formation control for swarms of unmanned aerial vehicles with motion constraints and unknown disturbances[J]. Chinese Journal of Aeronautics, 2020, 33 (11): 2972- 2988. doi: 10.1016/j.cja.2020.03.020
25	MERCADO D A, CASTRO R, LOZANO R. Quadrotors flight formation control using a leader-follower approach[C]//Proc. of the European Control Conference, 2013: 3858-3863.
26	MAHMOOD A , KIM Y . Leader-following formation control of quadcopters with heading synchronization[J]. Aerospace Science and Technology, 2015, 47, 68- 74. doi: 10.1016/j.ast.2015.09.009
27	李超兵, 包为民, 李忠奎, 等. 运载火箭推力故障不确定性下轨迹可达包络分析[J]. 宇航学报, 2023, 44 (1): 25- 33.
	LI C B , BAO W M , LI Z K , et al. Trajectory reachable envelope analysis of launch vehicle under thrust failure and uncertainties[J]. Journal of Astronautics, 2023, 44 (1): 25- 33.
28	GELLER D K . Linear covariance techniques for orbital rendezvous analysis and autonomous onboard mission planning[J]. Journal of Guidance, Control, and Dynamics, 2006, 29 (6): 1404- 1434. doi: 10.2514/1.19447
29	CHRISTENSEN R S , GELLER D . Linear covariance techniques for closed-loop guidance navigation and control system design and analysis[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2014, 228 (1): 44- 65. doi: 10.1177/0954410012467717
30	XING Y P , YIN Z Y , WEI C S , et al. Obstacle-avoidance safety- guaranteed rendezvous control with a velocity safety corridor and prescribed performance[J]. Journal of Aerospace Engineering, 2024, 37 (5): 4024054. doi: 10.1061/JAEEEZ.ASENG-5564
31	MACKTOOBIAN M , SHOOREHDELI M A . Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots[J]. Robotica, 2016, 34 (5): 1128- 1150. doi: 10.1017/S0263574714002100
32	PAN Z H , ZHANG C X , XIA Y Q , et al. An improved artificial potential field method for path planning and formation control of the multi-UAV systems[J]. IEEE Trans.on Circuits and Systems II: Express Briefs, 2021, 69 (3): 1129- 1133.
33	SHIN Y , KIM E . Hybrid path planning using positioning risk and artificial potential fields[J]. Aerospace Science and Technology, 2021, 112, 106640. doi: 10.1016/j.ast.2021.106640

变量名称	数值
σ_η/(m·s^－2)	[0.01, 0.1, 0.06]^T
τ_η/s	[0.01, 0.2, 0.3]^T
动态障碍物初始状态	[7, 10, 1, 0.35, 0, 0]^T

变量名称	数值
最大线速度/(m·s^-1)	1
最小线速度/(m·s^-1)	0.1
俯仰角最大变化值/rad	1.047 2
偏航角最大变化值/rad	1.047 2
领航者初始状态	[－5, －5, 1, 0.5, 0, 0.78]^T
跟随者1初始状态	[－8.48, －5.65, 1, 0.5, 0, 0.78]^T
跟随者2初始状态	[－5.65, －8.84, 1, 0.5, 0, 0.78]^T
领航者目标位置/m	[35, 35, 1]^T
机间相对期望距离/m	4
机间相对期望夹角/rad	[2.618 0, 3.665 2]^T

变量名称	本文方法	传统人工势场法	减少百分比/%
领航者飞行路径长度/m	58.01	65.7	11.7
跟随者1飞行路径长度/m	60.9	78.9	22.8
跟随者2飞行路径长度/m	59.4	75.2	20.2
平均编队误差/m	22.6	69.5	67.5
飞行总时间/s	118	139	15.1

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