双属性概率图优化的无人机集群协同目标搜索

doi:10.3969/j.issn.1001-506X.2020.01.16

摘要/Abstract

摘要：

为了提高不确定环境下无人机（unmanned aerial vehicle，UAV）对目标捕获能力，进而提高多UAV协同搜索效率，提出了基于双属性概率图结合改进的协同进化遗传算法（improved co-evolutionary genetic algorithm，ICEGA）的多UAV协同目标搜索方法。首先，根据环境的先验信息，在原概率图基础上引入标志位，建立基于双属性矩阵的待搜索环境概率模型，提高环境和目标的信息感知准确度；其次，定义UAV的飞行规则并结合目标先验概率图信息，建立UAV运动模型及确定最大收益的目标函数；最后，建立分布式UAV之间的信息交互模型，运用ICEGA算法优化产生最优协同决策输入航向角集合，在线实时滚动优化产生最优协同路径。实验结果表明，基于双属性概率图结合ICEGA算法更能够保证最优路径的产生，使得UAV能够准确地搜索到目标；同时，对比仿真验证了ICEGA算法能够提高UAV之间的协同性，保证了路径可行性及提高了目标搜索效率。

关键词: 多无人机, 协同搜索, 改进的协同进化遗传算法, 双属性概率图, 滚动优化

Abstract:

To improve the target capture capability of unmanned aerial vehicle (UAV) in uncertain environments and the efficiency of multi-UAVs collaborative searching, the proposed method named improved co-evolutionary genetic algorithm (ICEGA) based on dual attribute probability maps is proposed for multi-UAVs cooperative target capturing. Firstly, according to the prior information of the environment, the flag bit is introduced to the original probability map, and the proposed environment probability model is built. Secondly, the flight rules of UAVs and the environment probability model are used together to establish the UAV motion model and the objective function. Finally, the distributed model between the UAVs is established, and the ICEGA algorithm is used to optimize the input heading angles set with cooperative decision-making. The experimental results show that the dual-attribute probability map working with the ICEGA algorithm can guarantee the optimal searching path, so that the UAV can accurately capture the target. At the same time, the comparable experiments prove that the ICEGA algorithm can improve the coordination between UAVs, make the flying path feasible and improve the efficiency of target capturing.

Key words: multi-unmanned aerial vehicles (UAVs), collaborative search, improved co-evolutionary genetic algorithm (ICEGA), dual attribute map, rolling optimization

中图分类号:

V279
TP2

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.

Jie HUANG, Wei SUN, Yu GAO. Cooperative searching for the multi-UAVs based on dual-attribute probability model optimization[J]. Systems Engineering and Electronics, 2020, 42(1): 118-127.

图/表 24

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

表2

表3

图10

图11

图12

表4

表5

图13

图14

图15

图16

图17

图18

图19

参考文献 30

1	CHEN Y , YANG D , YU J . Multi-UAV task assignment with parameter and time-sensitive uncertainty using modified two-part wolf pack search algorithm[J]. IEEE Trans.on Aerospace and Electronic Systems, 2018, 54 (6): 2853- 2872. doi: 10.1109/TAES.2018.2831138
2	WU W N , WANG X G , CUI N G . Fast and coupled solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles[J]. Aerospace Science and Technology, 2018, 79 (1): 131- 144.
3	HU J , XIE L , XU J , et al. Multi-agent co-operative target search[J]. Sensors, 2014, 14 (6): 9408- 9428. doi: 10.3390/s140609408
4	THEYSABACD C . Using a TSP heuristic for routing order pickers in warehouses[J]. European Journal of Operational Research, 2010, 200 (3): 755- 763. doi: 10.1016/j.ejor.2009.01.036
5	彭辉, 苏菲, 沈林成. 用于多无人机广域目标搜索的扩展搜索图方法[J]. 系统工程与电子技术, 2010, 32 (4): 795- 798.
	PENG H , SU F , SHEN L C . Extended search map approach for multiple UAVs wide area target searching[J]. Systems Engineering and Electronics, 2010, 32 (4): 795- 798.
6	TAVASSOLIPOUR M , MOTAHARI S A , SHALMANI M T . Learning of Gaussian processes in distributed and communication limited systems[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2017, PP (99): 1- 14.
7	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.
8	YAO P , WANG H L , SU Z K . UAV feasible path planning based on disturbed fluid and trajectory propagation[J]. Chinese Journal of Aeronautics, 2015, 5 (4): 1163- 1176.
9	UDOMSAKDIGOOL A , KACHITVIC H V . Multiple colony ant algorithm for job-shop scheduling problem[J]. International Journal of Production Research, 2008, 46 (15): 4155- 4175. doi: 10.1080/00207540600990432
10	BASHIR Z A , ELHAWARY M E . Applying wavelets to short-term load forecasting using PSO-based neural networks[J]. IEEE Trans.on Power Systems, 2009, 24 (1): 20- 27.
11	GONG Y J , LI J J , ZHOU Y , et al. Genetic learning particle swarm optimization[J]. IEEE Trans.on Cybernetics, 2017, 46 (10): 2277- 2290.
12	LIU Z , GAO X G , FU X W . A cooperative search and coverage algorithm with control lable revisit and connectivity maintenance for multiple unmanned aerial vehicles[J]. Sensors, 2018, 18 (5): 1472- 1506.
13	TONG L , LI X G , HU J H , et al. A PSO optimization scale-transformation stochasti resonance algorithm with stability mutation operator[J]. IEEE Access, 2017, PP (99): 1167- 1176.
14	ZHANG M , LIU H H . Cooperative tracking a moving target using multiple fixed-wing UAVs[J]. Journal of Intelligent&Robotic Systems, 2016, 81 (3/4): 505- 529.
15	HU X X , LIU Y H , WANG G Q . Optimal search for moving targets with sensing capabilities using multiple UAVs[J]. Journal of Systems Engineering and Electronics, 2017, 28 (3): 526- 535. doi: 10.21629/JSEE.2017.03.12
16	ZHEN Z Y , XING D J , GAO C . Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm[J]. Aerospace Science and Technology, 2018, 76, 402- 411. doi: 10.1016/j.ast.2018.01.035
17	DETIENNE B , QUADRI D , RODRIGUES C D . Two phase solution for an intelligent moving target search problem based on a 0-1 linear model[J]. International Journal of Production Research, 2014, 52 (24): 7177- 7192. doi: 10.1080/00207543.2014.970714
18	CONNELL A , FLYNN D , KEANE A . Rolling multi-period optimization to control electric vehicle charging in distribution networks[J]. IEEE Trans.on Power Systems, 2014, 29 (1): 340- 348.
19	沈东, 魏瑞轩, 祁晓明, 等. 基于MTPM和DPM的多无人机协同广域目标搜索滚动时域决策[J]. 自动化学报, 2014, 40 (7): 1391- 1403.
	SHEN D , WEI R X , QI X M , et al. Receding horizon decision method based on MTPM and DPM for multi-UAVs cooperative large area target search[J]. Acta Automatica Sinica, 2014, 40 (7): 1391- 1403.
20	HILL B D , KALISCHUK M , WATERER D R , et al. An environmental model predicting bacterial ring rot symptom expression[J]. American Journal of Potato Research, 2011, 88 (3): 294- 301. doi: 10.1007/s12230-011-9193-4
21	PENDERS J , ALBOUL L , WITKOWSKI U , et al. A robot swarm assisting a human fire-fighter[J]. Advanced Robotics:The International Journal of the Robotics Society of Japan, 2011, 25 (1/2): 93- 117.
22	LIU Y Y , MAXIMILIAN M J , ZELAZO D , et al. A distributed control approach to form ation balancing and maneuvering of multiple multirotor UAVs[J]. IEEE Trans.on Robotics, 2018, 34 (4): 870- 882. doi: 10.1109/TRO.2018.2853606
23	FEI W , ZHOU L , HUI R , et al. Multi-objective optimization model of source-load storage synergetic dispatch for a building energy management system based on TOU price demand response[J]. IEEE Trans.on Industry Applications, 2018, 54 (2): 1017- 1028. doi: 10.1109/TIA.2017.2781639
24	MATTHEW C , TOM F , WEN H C , et al. Optimal polygon decomposition for UAV survey coverage path planning in wind[J]. Sensors, 2018, 18 (7): 2132- 2159. doi: 10.3390/s18072132
25	JUANG C F , YEH Y T . Multiobjective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks[J]. IEEE Trans.on Cybernetics, 2018, 48 (6): 1910- 1922. doi: 10.1109/TCYB.2017.2718037
26	WU Y W , SUI Y , WANG G . Vision-based real-time aerial object localization and tracking for UAV sensing system[J]. IEEE Access, 2017, 5, 23969- 23978. doi: 10.1109/ACCESS.2017.2764419
27	CORTES P , KAZMIERKOWSKI M P , KENNEL R M , et al. Predictive control in power electronics and drives[J]. IEEE Trans.on Industrial Electronics, 2008, 55 (12): 4312- 4324. doi: 10.1109/TIE.2008.2007480
28	YI L , JUN Y , BIN K . Static aeroelastic optimisation to wing structural weight estimation of an extremely manoeuvrable UAV[J]. The Aeronautical Journal, 2015, 119 (1218): 1033- 1043. doi: 10.1017/S0001924000004310
29	SONG Y , HUANG X , WEN C . Robust ada-ptive fault-tolerant PID control of MIMO non-linear systems with unknown control direction[J]. IEEE Trans.on Industrial Electronics, 2017, 64 (6): 4876- 4884. doi: 10.1109/TIE.2017.2669891
30	JAMESABC T . A cooperative parallel tabu search algorithm for the quadratic assignment problem[J]. European Journal of Operational Research, 2009, 195 (3): 810- 826.

Γ₁	Γ₂	ω₁	ω₂	目标数目
0.45	0.2	0.15	0.2	7
0.35	0.2	0.2	0.25	5
0.30	0.15	0.25	0.3	6

UAV	网格位置(m, n)	航向角/(°)	编码
UAV₁	(1, 1)	45	1
UAV₂	(1, 3)	0	2

目标	位置(x, y)/m	网格位置(m, n)	属性p/(p, f)
T₁	(1 500, 800)	(15, 8)	0.8/(0.8, 1)
T₂	(1 200, 1 400)	(12, 14)	0.8/(0.8, 1)
T₃	(900, 800)	(9, 8)	0.8/(0.8, 1)

UAV	网格位置(m, n)	初始航向角ϕ/(°)	编码
UAV₁	(1, 1)	90	0
UAV₂	(1, 3)	0	2
UAV₃	(1, 11)	45	1

目标	位置(x, y)/m	网格位置(m, n)	属性(p, f)
T₁	(1 500, 800)	(15, 8)	(0.8, 1)
T₂	(1 200, 1 400)	(12, 14)	(0.8, 1)
T₃	(900, 800)	(9, 8)	(0.8, 1)
T₄	(600, 1600)	(6, 16)	(0.8, 1)
T₅	(300, 300)	(3, 3)	(0.8, 1)