[1]梁星星,黄魁华,马 扬,等.周界防护的最优替换调度[J].南京师范大学学报(自然科学版),2019,42(03):52-57.[doi:10.3969/j.issn.1001-4616.2019.03.007]
 Liang Xingxing,Huang Kuihua,Ma Yang,et al.Optimal Replacement Scheduling for Perimeter Guarding[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):52-57.[doi:10.3969/j.issn.1001-4616.2019.03.007]
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周界防护的最优替换调度()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
52-57
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Optimal Replacement Scheduling for Perimeter Guarding
文章编号:
1001-4616(2019)03-0052-06
作者:
梁星星1黄魁华1马 扬1陈 超1孙博良1马 豪2张广平3黄红蓝1
(1.国防科技大学系统工程学院,湖南 长沙 410072)(2.中国西安卫星测控中心,陕西 西安 710043)(3.中国人民解放军31111部队,江苏 南京 210023)
Author(s):
Liang Xingxing1Huang Kuihua1Ma Yang1Chen Chao1Sun Boliang1Ma Hao2Zhang Guangping3Huang Honglan1
(1.College of Systems Engineering,National University of Defense Technology,Changsha 410072,China)(2.China Xi’an Satellite Control Center,Xi’an 710043,China)(3.The PLA 31111 Troops,Nanjing 210023,China)
关键词:
机器学习调度问题最优替换策略周界防卫UAV
Keywords:
machine learningscheduling problemoptimal replacement strategiesperimeter guardingUAV
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.007
文献标志码:
A
摘要:
随着UAV技术的不断发展,人们越来越趋向于采用带有摄像功能的UAV来进行区域的周界防卫,从而跟踪监测潜在的入侵或其他监视任务. 考虑UAV的电量消耗问题,本文提出了一种替换策略来保证UAV对的跟踪电量尽可能大,给出了周期性策略的重要性说明,在这一指导下,提出了UAV数量为奇数和偶数时的替换策略最优的充要条件,并给出了生成最优策略的方法.
Abstract:
With the continuous development of UAV technology,people are more and more inclined to use UAV with camera function to defend the perimeter of the area,so as to track and monitor potential intrusion or other surveillance tasks. Considering the power consumption of UAV,this paper proposesed a replacement strategy to ensure that the tracking power of UAV pairs is as large as possible. The importance of periodic strategy is explained. Under this guidance,the necessary and sufficient conditions for the optimal replacement strategy when the number of UAV is odd and even are proposed,and the method for generating the optimal strategy is given.

参考文献/References:

[1] BULLO F,CORTES J,MARTINEZ S. Distributed control of robotic networks[J]. Dissertations & theses-gradworks,2008(1):320-335.
[2]MOLYBOHA A,ZABARANKIN M. Stochastic optimization of sensor placement for diver detection[J]. INFORMS,2012,60(2):292-312.
[3]AGMON N,KAMINKA G A,KRAUS S. Multi-robot adversarial patrolling:facing a full-knowledge opponent[J]. AI access foundation,2011,42:887-916.
[4]ACEVEDO J J,ARRUE B C,MAZA I,et al. Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions[J]. Journal of intelligent & robotic systems,2013,70(1/4):329-345.
[5]BURDAKOV O,KVARNSTROM J,DOHERTY P. Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles[J]. Annals of operations research,2014,249(1/2):1-12.
[6]ERDELJ M,SAIF O,NATALIZIO E,et al. UAVs that fly forever:uninterrupted structural inspection through automatic UAV replacement[EB/OL]. [2019-03-04]. https://dio.org/10.1016/j.adhoc.2017.11.012.
[7]KIM D,LEE K,MOON I. Stochastic facility location model for drones considering uncertain flight distance[EB/OL]. [2019-03-04]. https://dio.org/10.1007/s10479-018-3114-6.
[8]HARTUV E,AGMON N,KRAUS S,et al. Scheduling spare drones for persistent task performance under energy constraints[C]//Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. Liverpool:International foundation for autonomous agents and multiagent systems,2018:532-540.
[9]CUMINO P,LOBATO JUNIOR W,TAVARES T,et al. Cooperative UAV scheme for enhancing video transmission and global network energy efficiency[J]. Sensors,2018,18(12):4155.
[10]段晓稳,高晓光,李波. 综合作战区同构舰载预警机巡逻策略分段滚动规划方法研究[J]. 电子学报,2017,45(6):1301-1310.

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备注/Memo

备注/Memo:
收稿日期:2019-07-05.基金项目:国家自然科学基金(71471174)、装备发展部人工智能应用技术领域基金(61403120206). 通讯联系人:黄魁华,博士,副研究员,研究方向:智能规划. E-mail:kuihh@163.com
更新日期/Last Update: 2019-09-30