局部通信条件下多无人机协同搜索方法

doi:10.12305/j.issn.1001-506X.2022.12.23

摘要/Abstract

摘要：

针对复杂场景中障碍物、电磁干扰等因素造成的无人机(unmanned aerial vehicle, UAV)编队部分通信链路不可达问题, 提出局部通信条件下的协同搜索(cooperative search under local communication, CSLC)方法。首先, 根据各UAV的实时位置计算当前时刻编队通信拓扑关系, 建立UAV编队的局部互通网络。在此基础上, 综合考虑UAV通信能力和编队协同目标搜索能力, 构建基于通信链路稳定收益和协同编队收益的协同搜索模型。最后, 设定多UAV编队所需的安全距离和最大运动速率的约束条件, 保证模型能够得到最优路径可行解, 提高多UAV协同搜索效率。实验结果表明, 与现有的目标搜索方法相比, CSLC方法在保证协同搜索路径可行性的同时, 提升了多UAV协同搜索的能力。

关键词: 局部通信, 通信拓扑关系, 目标搜索, 协同编队

Abstract:

Aiming at the problem of inaccessible local communication link for unmanned aerial vehicle (UAV) formation caused by obstacles and electromagnetic interference in complex scenes, a cooperative search under local communication (CSLC) method is proposed. Firstly, according to the real-time location of each UAV, the communication topology relation at the current time is calculated, thereby establishing a local intercommunication network of UAV formation. On this basis, considering the UAV communication capability and the formation cooperative target search capability, the cooperative search model based on the communication link stability benefit and the cooperative formation revenue is constructed. Finally, the constraints of the safety distance and maximum movement speed required for UAV formation are set to ensure that the model can obtain the feasible solution of the optimal path and improve the efficiency of multi-UAV cooperative search. The experimental results show that, compared with the existing target search methods, the CSLC method not only ensures the feasibility of cooperative path search, but also enhances the capability of multi-UAV cooperative search.

Key words: local communication, communication topological relation, target search, cooperative formation

中图分类号:

TN959

朱晓敏, 刘大千, 费博雯, 门通. 局部通信条件下多无人机协同搜索方法[J]. 系统工程与电子技术, 2022, 44(12): 3783-3791.

Xiaomin ZHU, Daqian LIU, Bowen FEI, Tong MEN. Cooperative search method for multiple UAVs under local communication[J]. Systems Engineering and Electronics, 2022, 44(12): 3783-3791.

图/表 15

图1

图2

图3

图4

表1

表2

表3

图5

图6

图7

表4

图8

表5

图9

图10

参考文献 30

1	DAI M H , SU Z , XU Q C , et al. Vehicle assisted computing offloading for unmanned aerial vehicles in smart city[J]. IEEE Trans.on Intelligent Transportation Systems, 2021, 22 (3): 1932- 1944. doi: 10.1109/TITS.2021.3052979
2	BAI L , LIU J X , WANG J X , et al. Data aggregation in UAV-aided random access for the internet of vehicle[J]. IEEE Internet of Things Journal, 2022, 9 (8): 5755- 5764. doi: 10.1109/JIOT.2021.3063734
3	曾国奇, 白宇, 林伟, 等. 地面运动目标的多UAV协同搜索方法[J]. 系统工程与电子技术, 2018, 40 (7): 1498- 1505.
	ZENG G Q , BAI Y , LIN W , et al. Multi-UAV cooperative search method for ground moving targets[J]. Systems Engineering and Electronics, 2018, 40 (7): 1498- 1505.
4	符小卫, 魏广伟, 高晓光. 不确定环境下多无人机协同区域搜索算法[J]. 系统工程与电子技术, 2016, 38 (4): 821- 827.
	FU X W , WEI G W , GAO X G . Cooperative area search algorithm for multi-UAVs in uncertainty environment[J]. Systems Engineering and Electronics, 2016, 38 (4): 821- 827.
5	YU H , MEIER K , ARGYLE M , et al. Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles[J]. IEEE Trans.on Mechatronics, 2015, 20 (2): 541- 552. doi: 10.1109/TMECH.2014.2301459
6	吴岸平, 郭正, 侯中喜, 等. 不确定环境下无人机区域目标搜索及载荷参数影响[J]. 国防科技大学学报, 2020, 42 (4): 35- 42.
	WU A P , GUO Z , HOU Z X , et al. Area target search and payload parameters influence for UAV in uncertain environment[J]. Journal of National University of Defense Technology, 2020, 42 (4): 35- 42.
7	杨春宁, 杜黎明, 李春. 未知区域无人机协同搜索方法及效率分析[J]. 航空科学技术, 2019, 30 (10): 56- 63.
	YANG C N , DU L M , LI C . Methods and efficiency comparison of UAV swarms collaborative search in unknown area[J]. Aeronautical Science and Technology, 2019, 30 (10): 56- 63.
8	刘重, 高晓光, 符小卫. 带信息素回访机制的多无人机分布式协同目标搜索[J]. 系统工程与电子技术, 2017, 39 (9): 1998- 2011.
	LIU C , GAO X G , FU X W . Multi-UAVs distributed cooperative target search algorithm with controllable revisit mechanism based on digital pheromone[J]. Systems Engineering and Electronics, 2017, 39 (9): 1998- 2011.
9	黄杰, 孙伟, 高渝. 双属性概率图优化的无人机集群协同目标搜索[J]. 系统工程与电子技术, 2020, 42 (1): 118- 127.
	HUANG J , SUN W , GAO Y . Cooperative searching for the multi-UAVs based on dual-attribute probability model optimization[J]. Systems Engineering and Electronics, 2020, 42 (1): 118- 127.
10	BO N, LI X M, DAI J J, et al. A hierarchical optimization strategy of trajectory planning for multi-UAVs[C]//Proc. of the 9th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2017: 294-298.
11	YAO W Y , QI N M , WAN N , et al. An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles[J]. Aerospace Science and Technology, 2019, 86, 455- 464.
12	RADMANESH R , KUMAR M , FRENCH D , et al. Towards a PDE-based large-scale decentralized solution for path planning of UAVs in shared airspace[J]. Aerospace Science and Technology, 2020, 105, 105965.
13	YANG C J, LIU L F, WU J. Path planning algorithm for small UAV based on dubins path[C]//Proc. of the IEEE/CSAA International Conference on Aircraft Utility Systems, 2016: 1144-1148.
14	SONG X Q, HU S Q. 2D path planning with Dubins-path-based a algorithm for a fixed-wing UAV[C]//Proc. of the IEEE International Conference on Control Science and Systems Engineering, 2017: 69-73.
15	CARABAZA S , BESADA P E , LPREZ O J , et al. Ant colony optimization for multi-UAV minimum time search in uncertain domains[J]. Applied Soft Computing, 2018, 62, 789- 806.
16	HUANG L W , QU H , JI P , et al. A novel coordinated path planning method using K-degree smoothing for multi-UAVs[J]. Applied Soft Computing, 2016, 48, 182- 192.
17	ZHU S C , GUI L , CHENG N , et al. Joint design of access point selection and path planning for UAV-assisted cellular networks[J]. IEEE Internet of Things Journal, 2020, 7 (1): 220- 233.
18	LI J , XIONG Y H , SHE J H , et al. A path planning method for sweep coverage with multiple UAVs[J]. IEEE Internet of Things Journal, 2020, 7 (9): 8967- 8978.
19	YAO P , WANG H L , JI H X . Multi-UAVs tracking target in urban environment by model predictive control and improved grey wolf optimizer[J]. Aerospace Science and Technology, 2016, 55, 131- 143.
20	JUANG C F , YEH Y T . Multi-objective evolution of biped robot gaits using advanced continuous ant-colon optimized recurrent neural networks[J]. IEEE Trans.on Cybernetics, 2018, 48 (6): 1910- 1922.
21	SHAO Y , ZHAO Z F , LI R P , et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21 (20): 796- 808.
22	吴傲, 杨任农, 梁晓龙, 等. 基于信息素决策的无人机集群协同搜索算法[J]. 北京航空航天大学学报, 2021, 47 (4): 814- 827.
	WU A , YANG R N , LIANG X L , et al. Cooperative search algorithm based on pheromone decision for UAV swarm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (4): 814- 827.
23	LI J , XIONG Y H , SHE J H , et al. A path planning method for sweep coverage with multiple UAVs[J]. IEEE Internet of Things Journal, 2020, 7 (9): 8967- 8978.
24	BO N, LI X M, DAI J J, et al. A hierarchical optimization strategy of trajectory planning for multi-UAVs[C]//Proc. of the 9th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2017: 294-298.
25	肖东, 江驹, 周俊, 等. 通信受限下多无人机协同运动目标搜索[J]. 哈尔滨工程大学学报, 2018, 39 (11): 1823- 1829.
	XIAO D , JIANG J , ZHOU J , et al. Multi-UAV cooperation search for moving targets under limited communication[J]. Journal of Harbin Engineering University, 2018, 39 (11): 1823- 1829.
26	PARADZIK M, INCE G. Multi-agent search strategy based on digital pheromones for UAVs[C]//Proc. of the 24th Signal Processing and Communication Application Conference, 2016: 233-236.
27	KASHINO Z , NEJAT G , BENHABIB B . A hybrid strategy for target search using static and mobile sensors[J]. IEEE Trans.on Cybernetics, 2020, 50 (2): 856- 868.
28	LI F , ZHOU M C , DING Y S . An adaptive online co-search method with distributed samples for dynamic target tracking[J]. IEEE Trans.on Control Systems Technology, 2018, 26 (2): 439- 451.
29	JIANG L , YANG X B . Study on enlarging the searching scope of staring area and tracking imaging of dynamic targets by optical satellites[J]. IEEE Sensors Journal, 2021, 21 (4): 5349- 5358.
30	QU Y H, SUN Y, WANG K, et al. Multi-UAV cooperative search method for a moving target on the ground or sea[C]//Proc. of the Chinese Control Conference, 2019: 4049-4054.

指标参数	吸引信息素	排斥信息素
挥发系数	G_a=0.3	G_r=0.3
传播系数	E_a=0.4	E_r=0.4
释放常量	D_a=1	D_r=10

无人机	初始位置	网格索引	初始方向
U₁	(50, 750)	(1, 15)	4
U₂	(50, 1 500)	(1, 30)	0
U₃	(50, 2 250)	(1, 45)	4
U₄	(750, 50)	(15, 1)	6
U₅	(1 500, 50)	(30, 1)	6
U₆	(2 250, 50)	(45, 1)	2
U₇	(3 050, 50)	(61, 1)	0
U₈	(3 050, 750)	(61, 15)	4
U₉	(3 050, 1 500)	(61, 30)	0
U₁₀	(750, 3 050)	(15, 61)	6
U₁₁	(1 500, 3 050)	(30, 61)	4
U₁₂	(2 250, 3 050)	(45, 61)	4

目标	初始位置	网格索引	初始方向
T₁	(2 250, 750)	(45, 15)	4
T₂	(2 250, 1 550)	(45, 31)	0
T₃	(2 250, 2 250)	(45, 45)	4
T₄	(2 250, 1 650)	(45, 33)	4
T₅	(1 650, 1 650)	(33, 33)	2
T₆	(850, 1 650)	(17, 33)	4
T₇	(850, 850)	(17, 17)	0
T₈	(1 650, 850)	(33, 17)	0
T₉	(2 250, 850)	(45, 17)	0

搜索方法	迭代周期/s	目标识别数/个	区域覆盖率/%
PMGA	100	1	36.59
	200	4	59.31
	300	5	73.14
DMPC	100	2	41.14
	200	4	59.90
	300	7	73.58
CSLC	100	3	47.06
	200	6	61.04
	300	9	80.20

搜索方法	迭代周期/s	目标识别数/个	区域覆盖率/%
r=10	100	2	40.26
	200	3	57.61
	300	6	72.36
r=20	100	3	47.06
	200	6	61.04
	300	9	80.20
r=30	100	4	46.33
	200	7	61.42
	300	9	80.48
r=40	100	4	46.12
	200	8	62.78
	300	9	79.35