An Adaptive Scale Control Method of Multiple UAVs for Persistent Surveillance

被引：0
|
作者：
Jing T. [1 ]
Wang T. [1 ]
Wang W. [1 ]
Li X. [1 ]
Zhou X. [1 ]
机构：
[1] College of Systems Engineering, National University of Defense Technology, Changsha
来源：
Wang, Tao (wangtao1976@nudt.edu.cn) | 2018年 / Science Press卷 / 55期
关键词：
Digital turf; Dynamic deployment; Persistent surveillance; Scale control; Unmanned aerial vehicles;
D O I：
10.7544/issn1000-1239.2018.20170311
中图分类号：
学科分类号：
摘要：
Unmanned aerial vehicles swarm persistent surveillance is an important application in the multiple unmanned aerial vehicles (UAVs). With the increasing complexity of environment and tasks in surveillance mission,the requirement of UAV swarm reconfiguration and flexibility is also rising. To the adaptive and reconfigurable UAVs swarm, the amount of UAV is one of the basic control factors. However, most studies in UAV swarm control focus on control cooperative path planning in given mission, while dynamic deployment of the UAV amount in swarm system is neglected. In the surveillance design of traditional UAVs swarm, the amount of swarm is hard to adaptively adjust to match the different surveillance environments and various situations. To solve this kind of problem, a "digital turf" variation model is proposed on the base of the regional information entropy. Moreover, we imitate a dynamic balancing mechanism in the turf-herbivore ecosystem and design the scale control method in target region-UAV swarm. What's more, on this basis, we study the biomes matrix and equilibrium point situation when surveillance system reaches stable and discusses adaptive adjusting method of UAV swarm in different mission environments with different efficiency constraints. Finally, the existence of equilibrium point and the convergence of system are demonstrated by simulation. © 2018, Science Press. All right reserved.
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页码：1254 / 1262
页数：8
相关论文
共 16 条
  • [1] Shen L., Niu Y., Zhu H., Theories and Methods of Autonomous Cooperation Control for Multiple UAVs, (2013)
  • [2] Leahy K., Zhou D., Vasile C.I., Et al., Provably Correct Persistent Surveillance for Unmanned Aerial Vehicles Subject to Charging Constraints, pp. 605-619, (2016)
  • [3] Peng H., Su F., Shen L., Extended search map approach for multiple UAVs wide area target searching, Journal of Systems Engineering and Electronics, 7, 4, pp. 795-798, (2010)
  • [4] Saska M., Vonasek V., Chudoba J., Et al., Swarm distribution and deployment for cooperative surveillance by micro-aerial vehicles, Journal of Intelligent & Robotic Systems, 84, 1-4, pp. 469-492, (2016)
  • [5] Liang X., Sun Q., Yin Z., Et al., Review on large-scale unmanned system swarm intelligence control method, Application Research of Computers, 32, 1, pp. 11-16, (2015)
  • [6] Lin L., Goodrich M.A., Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning, IEEE Trans on Cybernetics, 44, 12, pp. 2532-2544, (2014)
  • [7] Shen D., Wei R., Qi X., Et al., Receding horizon decision method based on MTPM and DPM for multi-UAVs cooperative large area target search, Acta Automatica Sinica, 40, 7, pp. 1391-1403, (2014)
  • [8] Bernard E., Friedt J.M., Tolle F., Et al., Using a small COTS UAV to quantify moraine dynamics induced by climate shift in Arctic environments, International Journal of Remote Sensing, 38, 8-10, pp. 2480-2494, (2016)
  • [9] Balampanis F., Maza I., Ollero A., Coastal areas division and coverage with multiple UAVs for remote sensing, Sensors, 17, 4, pp. 808-833, (2017)
  • [10] Zhang X., Chen L., Neumann A.U., The stage-structured predator-prey model and optimal harvesting policy, Mathematical Biosciences, 168, 2, pp. 201-210, (2000)
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