面向多无人平台区域监视任务的信息素正向激励栅格方法

doi:10.12382/bgxb.2022.0537

摘要/Abstract

摘要：

密集城市地区作战普遍存在区域监视问题,为使我方撤出最高危险区域以减轻损伤或减少人力消耗,采用无人系统执行侦察监视任务极具军事意义和应用价值。面向环境复杂多变且多无人平台初始位置邻近情况下的协同监控任务,针对现有的控制策略在遍历目标空间时多无人平台容易发生冲突的不足且缺少对多无人平台初始位置邻近情况的研究,在半启发式控制策略和栅格法的基础上通过引入信息素改进目标函数并制定冲突消解规则,构建出信息素正向激励栅格法。试验结果表明,信息素正向激励栅格法在冲突消解方面的表现优于现有控制策略,综合性能表现较好,特别在障碍物数量较多时全局平均空闲时间的表现更好,所提方法的有效性和合理性得到了验证。

关键词: 多无人平台, 协同监控, 半启发式控制, 信息素

Abstract:

There is a common problem of regional surveillance in operations in densely populated urban areas. In order to evacuate our side from the highest risk area to reduce damage or manpower consumption, the use of unmanned systems to carry out reconnaissance and surveillance tasks is of great military significance and application value. Aiming at collaborative monitoring tasks with complex and ever-changing environments and multiple unmanned platforms with adjacent initial positions, in response to the shortcomings of existing control strategies that are prone to conflicts when traversing the target space and the lack of research on the proximity of initial positions of multiple unmanned platforms, based on semi heuristic control strategies and grid methods, the objective function is improved by introducing pheromones and developing conflict resolution rules, a pheromone positive incentive grid method is constructed. Experimental results showed that the proposed method performed better in conflict resolution than existing control strategies, its overall performance was better, and the global average idle time was better especially when there were many obstacles. The effectiveness and rationality of the proposed method had been verified.

Key words: multi unmanned platforms, cooperative surveillance, semi-heuristic control, pheromone

中图分类号:

TP242

陈亚萍, 王楠, 洪华杰, 刘召阳, 闫响达. 面向多无人平台区域监视任务的信息素正向激励栅格方法[J]. 兵工学报, 2023, 44(9): 2859-2870.

CHEN Yaping, WANG Nan, HONG Huajie, LIU Zhaoyang, YAN Xiangda. Pheromone Positive Incentive Grid Method for Multi-unmanned Platform Regional Surveillance Task[J]. Acta Armamentarii, 2023, 44(9): 2859-2870.

图/表 15

图1 网格表示的环境模型

Fig.1 Environment model in grids

图2 相邻无人平台位置交换示意图

Fig.2 Schematic diagram of position exchange of adjacent unmanned platforms

图3 加入栅格法前后的对比图

Fig.3 Comparison diagram before and after grid method included

图4 冲突消解原理图

Fig.4 Conflict resolution schematic diagram

图5 自循环判断示意图

Fig.5 Self-loop judgement diagram

图6 持续监控流程示意图

Fig.6 Schematic diagram of the continuous monitoring process

图7 不同障碍物数量的情景1

Fig.7 Scenario 1 with different numbers of obstacles

图8 不同障碍物数量的情景2

Fig.8 Scenario 2 with different numbers of obstacles

图9 无人平台规划轨迹图

Fig.9 Diagram of planning trajectory of unmanned platform

表1 情景1 OBS取不同值时的冲突占比之和

Table 1 Sum of conflict shares when OBS takes different values in Scenario 1 %

算法	OBS=19	OBS=13	OBS=6
CR	100.7	100.1	100.1
HPCC	102.3	102.9	102.9
EVAP	34.9	51.6	41.3
CLInG	48.5	74.4	46.5
SGM	11.8	21.2	25.7
PPIGM	0.1	0.1	0.1

图10 情景1不同算法的全局平均空闲时间对比图

Fig.10 Comparison diagram of global average idle time of different algorithms in Scenario 1

图11 情景1 不同算法在全局平均空闲时间的箱线图

Fig.11 Box diagram of different algorithms of global average idle time in Scenario 1

表2 情景2 OBS取不同值时冲突占比之和

Table 2 Sum of conflict shares when OBS takes different values in Scenario 2 %

算法	OBS=19	OBS=13	OBS=6
CR	100	100.2	100
HPCC	100.4	105.2	100.4
EVAP	70.8	46.8	36.8
CLInG	61.2	62.5	57.7
SGM	8.2	22.3	24.2
PPIGM	0.1	0	0.1

图12 情景2不同算法的全局平均空闲时间对比图

Fig.12 Comparison diagram of global average idle time of different algorithms in Scenario 2

图13 情景2 不同算法在全局平均空闲时间的箱线图

Fig.13 Box diagram of different algorithms of global average idle time in Scenario 2

参考文献 23

[1]	谭民, 王硕, 曹志强. 多机器人系统[M]. 北京: 清华大学出版社, 2005.
	TAN M, WANG S, CAO Z Q. Multi-robot systems[M]. Beijing: Tsinghua University Press, 2005. (in Chinese)
[2]	TENG Z F, QIAN L D, HUANG J F. Multi-target localization algorithm for wireless sensor network[J]. Peer-to-Peer Networking and Applications, 2021, 14: 3452-3459. doi: 10.1007/s12083-021-01193-4
[3]	NGUYEN T G, PHAN T V, NGUYEN H H, et al. An efficient distributed algorithm for target-coverage preservation in wireless sensor networks[J]. Peer-to-Peer Networking and Applications, 2021, 14: 453-466. doi: 10.1007/s12083-020-00987-2
[4]	刘大鹍, 陈桂芬, 王义君. 自组织网络区域覆盖协作控制算法[J]. 兵工学报, 2020, 41(6): 1131-1139. doi: 10.3969/j.issn.1000-1093.2020.06.009
	LIU D K, CHEN G F, WANG Y J. Regional coverage cooperative control algorithm for Ad Hoc networks[J]. Acta Armamentarii, 2020, 41(6): 1131-1139. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.06.009
[5]	刘丽萍, 王智, 孙优贤. 无线传感器网络连接问题研究[J]. 兵工学报, 2007, 28(9):1096-1102.
	LIU L P, WANG Z, SUN Y X. Connectivity in wireless sensor networks[J]. Acta Armamentarii, 2007, 28(9):1096-1102. (in Chinese)
[6]	HUANG L, ZHOU M C, HAO K R, et al. A survey of multi-robot regular and adversarial patrolling[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(4): 894-903. doi: 10.1109/JAS.2019.1911537
[7]	ARRIBAS E, MANCUSO V, CHOLVI V. Coverage optimization with a dynamic network of drone relays[J]. IEEE Transactions on Mobile Computing, 2019, 19(10): 2278-2298. doi: 10.1109/TMC.7755 URL
[8]	WANG T, HUANG P F, DONG G Q. Modeling and path planning for persistent surveillance by unmanned ground vehicle[J]. IEEE Transactions on Automation Science and Engineering, 2020, 18(4): 1615-1625. doi: 10.1109/TASE.2020.3013288 URL
[9]	WANG T, HUANG P F, DONG G Q. Cooperative persistent surveillance on a road network by multi-UGVs with detection ability[J]. IEEE Transactions on Industrial Electronics, 2021, 69(11):11468-11478. doi: 10.1109/TIE.2021.3121729 URL
[10]	霍耀彦, 李宗刚, 高溥. 基于节点重要度的多机器人分布式巡逻策略[J]. 计算机应用研究, 2022, 39(2):510-514.
	HUO Y Y, LI Z G, GAO P. Distributed multi-robot patrolling strategy based on importance of nodes[J]. Application Research of Computers, 2022, 39(2): 510-514. (in Chinese)
[11]	WU Y, WU S B, HU X T. Cooperative path planning of UAVs & UGVs for a persistent surveillance task in urban environments[J]. IEEE Internet of Things Journal, 2020, 8(6): 4906-4919. doi: 10.1109/JIoT.6488907 URL
[12]	WU Y, WU S B, HU X T. Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments[J]. Complex & Intelligent Systems, 2021, 7:1633-1647.
[13]	NIGAM N, KROO I. Control and design of multiple unmanned air vehicles for a persistent surveillance task[C]//Proceedings of the 12nd AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Victoria, British Columbia, Canada:AIAA, 2008:5913.
[14]	NIGAM N, BIENIAWSKI S, KROO I, et al. Control of multiple UAVs for persistent surveillance: algorithm and flight test results[J]. IEEE Transactions on Control Systems Technology, 2011, 20(5):1236-1251. doi: 10.1109/TCST.2011.2167331 URL
[15]	NIGAM N. The multiple unmanned air vehicle persistent surveillance problem: a review[J]. Machines, 2014, 2(1):13-72. doi: 10.3390/machines2010013 URL
[16]	SEMPE F. Auto-organisation d'une collectivité de robots: application à l'activité de patrouille en présence de perturbations[D].Paris,France:University of Paris 6, 2004.
[17]	SEMPE F, DROGOUL A. Adaptive patrol for a group of robots[C] //Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003)(Cat. No. 03CH37453).Las Vegas,NV,US:IEEE, 2003, 3: 2865-2869.
[18]	CHU H N, GLAD A, SIMONIN O, et al. Swarm approaches for the patrolling problem, informationpropagation vs. pheromone evaporation[C]//Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence.Patras, Greece:IEEE, 2007, 1: 442-449.
[19]	ALAM T, RAHMAN M M, CARRILLO P, et al. Stochastic multi-robot patrolling with limited visibility[J]. Journal of Intelligent & Robotic Systems, 2020, 97:411-429.
[20]	ALMEIDA A L, CASTRO P M, MENEZES T R, et al. Combining idleness and distance to design heuristic agents for the patrolling task[C]//Proceedings of II Brazilian Workshop in Games and Digital Entertainment.Salvador, Brazil: Brazilian Computer Society, 2003: 33-40.
[21]	PORTUGAL D, ROCHA R P. Multi-robot patrolling algorithms:examining performance and scalability[J]. Advanced Robotics, 2013, 27(5): 325-336. doi: 10.1080/01691864.2013.763722 URL
[22]	欧阳鑫玉, 杨曙光. 基于势场栅格法的移动机器人避障路径规划[J]. 控制工程, 2014, 21(1):134-137.
	OUYANG X Y, YANG S G. Obstacle avoidance path planning of mobile robots based on potential grid method[J]. Control Engineering of China, 2014, 21(1):134-137. (in Chinese)
[23]	MACHADO A, RAMALHO G, ZUCKER J, et al. Multi-agent patrolling:an empirical analysis of alternative architectures[C]//Proceedings of International Workshop on Multi-Agent Systems and Agent-based Simulation. Berlin, Germany:Springer, 2002: 155-170.