探索与打击任务中异构智能体动态分组策略

陈晨; 吴啸尘; 陈杰; Panos M. PARDALOS; 丁舒忻

doi:10.1631/FITEE.2000352

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探索与打击任务中异构智能体动态分组策略

常规文章 | Updated：2022-04-19

- 探索与打击任务中异构智能体动态分组策略
  Enhanced Publication
- Dynamic grouping of heterogeneous agents for exploration and strike missions
- 信息与电子工程前沿（英文） 2022年23卷第1期页码：86-100
- Affiliations：
  
  1.School of Automation, Beijing Institute of Technology, Beijing100081, China
  2.Department of Control Science and Engineering, Tongji University, Shanghai200092, China
  3.Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL32611, USA
  4.Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing100081, China
- Author bio：
  
  E-mail: xiaofan@bit.edu.cn;
  ‡Corresponding authors
- Funds：
  
  National Natural Science Foundation of China(61773066);Foundation of China Academy of Railway Sciences Corporation Limited(2019YJ071)
- DOI：10.1631/FITEE.2000352
  中图分类号： TP391.9
- 纸质出版日期：2022-01-0 ，
  
  收稿日期：2020-07-17，
  
  录用日期：2020-11-16
- Accepted：
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陈晨, 吴啸尘, 陈杰, 等. 探索与打击任务中异构智能体动态分组策略[J]. 信息与电子工程前沿（英文）, 2022,23(1):86-100.

CHEN CHEN, XIAOCHEN WU, JIE CHEN, et al. Dynamic grouping of heterogeneous agents for exploration and strike missions. [J]. Frontiers of information technology & electronic engineering, 2022, 23(1): 86-100.
陈晨, 吴啸尘, 陈杰, 等. 探索与打击任务中异构智能体动态分组策略[J]. 信息与电子工程前沿（英文）, 2022,23(1):86-100. DOI： 10.1631/FITEE.2000352.

CHEN CHEN, XIAOCHEN WU, JIE CHEN, et al. Dynamic grouping of heterogeneous agents for exploration and strike missions. [J]. Frontiers of information technology & electronic engineering, 2022, 23(1): 86-100. DOI： 10.1631/FITEE.2000352.

摘要

多变的环境和复杂的作战任务对无人作战智能体任务群组的构建提出了新的要求。本文旨在解决新需求下的任务群组动态构建问题。针对智能体的异构性，在不断探索任务的情况下群体形成方法需满足能动态形成新的群组。提出一种融合了启发式规则和响应阈值模型的群组形成策略，用于动态调整任务群组的成员以适应新的任务需求。将任务需求与群组能力的匹配程度以及群组的组网开销作为评价团队素质的指标。选取响应阈值法和蚁群算法作为仿真实验中的对比算法。结果表明所提方法在解决动态任务组形成问题时具备一定优势。

Abstract

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents. This study aims to address the problem of dynamic construction of mission groups under new requirements. Agents are heterogeneous

and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored. In our method

a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions. The degree of matching between the mission requirements and the group's capabilities

and the communication cost of group formation are used as indicators to evaluate the quality of the group. The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations. The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

关键词

多智能体动态作战任务任务组形成启发式规则组网开销

Keywords

Multi-agentDynamic missionsGroup formationHeuristic ruleNetworking overhead

references

Butenko S,Murphey R,Pardalos PM,2003.Cooperative Control: Models, Applications and Algorithms.Springer,Dordrecht, the Netherlands.

Cui SG,Goldsmith AJ,Bahai A,2004.Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks.IEEE J Sel Areas Commun,22(6):1089-1098.doi:10.1109/JSAC.2004.830916http://doi.org/10.1109/JSAC.2004.830916

Ducatelle F,di Caro G,Förster A,et al.,2010.Adaptive navigation in a heterogeneous swarm robotic system.Proc 4th Int Conf on Cognitive Systems, p.87-94.

George JM,Sujit PB,Sousa JB,2010.Coalition formation with communication delays and maneuvering targets.Proc AIAA Guidance, Navigation, and Control Conf, p.28-32.doi:10.2514/6.2010-8422http://doi.org/10.2514/6.2010-8422

Gerkey BP,Matarić MJ,2004.A formal analysis and taxonomy of task allocation in multi-robot systems.Int J Robot Res,23(9):939-954.doi:10.1177/0278364904045564http://doi.org/10.1177/0278364904045564

Guo M,Xin B,Chen J,et al.,2020.Multi-agent coalition formation by an efficient genetic algorithm with heuristic initialization and repair strategy.Swarm Evol Comput,55:100686.doi:10.1016/j.swevo.2020.100686http://doi.org/10.1016/j.swevo.2020.100686

Hirsch MJ,Commander CW,Pardalos PM,et al.,2009.Optimization and Cooperative Control Strategies: Proceedings of the 8th International Conference on Cooperative Control and Optimization.Springer,Berlin, Germany.doi:10.1007/978-3-540-88063-9http://doi.org/10.1007/978-3-540-88063-9

Jiang RY,Ji W,Zheng BY,2010.Joint optimization of energy consumption in cooperative wireless sensor networks.J Electron Inform Technol,32(6):1475-1479(in Chinese).

Khan A,Aftab F,Zhang ZS,2019.Self-organization based clustering scheme for FANETs using glowworm swarm optimization.Phys Commun,36:100769.doi:10.1016/j.phycom.2019.100769http://doi.org/10.1016/j.phycom.2019.100769

Khoshnoud F,Esat II,de Silva CW,et al.,2019.Quantum network of cooperative unmanned autonomous systems.Unmanned Syst,7(2):137-145.doi:10.1142/S2301385019500055http://doi.org/10.1142/S2301385019500055

Kim MH,Baik H,Lee S,2014.Response threshold model based UAV search planning and task allocation.J Intell Robot Syst,75(3-4):625-640.doi:10.1007/s10846-013-9887-6http://doi.org/10.1007/s10846-013-9887-6

Kim Y,Gu DW,Postlethwaite I,2008.Real-time optimal time-critical target assignment for UAVs. In:Pardalos PM,Murphey R,Grundel D,et al. (Eds.),Advances in Cooperative Control and Optimization.Springer,Berlin, p.265-280,doi:10.1007/978-3-540-74356-9http://doi.org/10.1007/978-3-540-74356-9

Liu MJ,Lin J,Yuan YY,2013.Research of UAV cooperative reconnaissance with self-organization path planning.Proc Int Conf on Computer, Networks and Communication Engineering, p.207-213.doi:10.2991/iccnce.2013.51http://doi.org/10.2991/iccnce.2013.51

Manathara JG,Sujit PB,Beard RW,2011.Multiple UAV coalitions for a search and prosecute mission.J Intell Robot Syst,62(1):125-158.doi:10.1007/s10846-010-9439-2http://doi.org/10.1007/s10846-010-9439-2

Merabet GH,Essaaidi M,Talei H,et al.,2014.Applications of multi-agent systems in smart grids: a survey.Proc Int Conf on Multimedia Computing and Systems, p.1088-1094.doi:10.1109/ICMCS.2014.6911384http://doi.org/10.1109/ICMCS.2014.6911384

Moritz RLV,Middendorf M,2015.Decentralized and dynamic group formation of reconfigurable agents.Memet Comput,7(2):77-91.doi:10.1007/s12293-014-0149-3http://doi.org/10.1007/s12293-014-0149-3

Murphey R,Pardalos PM,2002.Cooperative Control and Optimization.Springer,Boston, USA.doi:10.1007/b130435http://doi.org/10.1007/b130435

Necsulescu P,Schilling K,2015.Automation of a multiple robot self-organizing multi-hop mobile ad-hoc network (MANET) using signal strength.Proc Int Instrumentation and Measurement Technology Conf, p.505-510.doi:10.1109/I2MTC.2015.7151319http://doi.org/10.1109/I2MTC.2015.7151319

Nejad MG,Kashan AH,2019.An effective grouping evolution strategy algorithm enhanced with heuristic methods for assembly line balancing problem.J Adv Manuf Syst,18(3):487-509.doi:10.1142/S0219686719500264http://doi.org/10.1142/S0219686719500264

Oh G,Kim Y,Ahn J,et al.,2018.Task allocation of multiple UAVs for cooperative parcel delivery.Proc Advances in Aerospace Guidance, Navigation and Control, p.443-454.doi:10.1007/978-3-319-65283-2_24http://doi.org/10.1007/978-3-319-65283-2_24

Orfanus D,de Freitas EP,Eliassen F,2016.Self-organization as a supporting paradigm for military UAV relay networks.IEEE Commun Lett,20(4):804-807.doi:10.1109/LCOMM.2016.2524405http://doi.org/10.1109/LCOMM.2016.2524405

Padmanabhan M,Suresh GR,2015.Coalition formation and task allocation of multiple autonomous robots.Proc 3rd Int Conf on Signal Processing, Communication and Networking, p.1-5.doi:10.1109/ICSCN.2015.7219891http://doi.org/10.1109/ICSCN.2015.7219891

Pardalos PM,Grundel D,Murphey R,et al.,2008.Cooperative Networks: Control and Optimization.Edward Elgar Publishing,Cheltenham, UK.

Ramchurn SD,Polukarov M,Farinelli A,et al.,2010.Coalition formation with spatial and temporal constraints.Proc 9th Int Conf on Autonomous Agents and Multiagent Systems, p.1181-1188.doi:10.5555/1838186.1838191http://doi.org/10.5555/1838186.1838191

Shehory O,Kraus S,1998.Methods for task allocation via agent coalition formation.Artif Intell,101(1-2):165-200.doi:10.1016/s0004-3702(98)00045-9http://doi.org/10.1016/s0004-3702(98)00045-9

Singh VK,Husaini S,Singh A,2010.Self-organizing agent coalitions in distributed multi-agent systems.Proc Int Conf on Computational Intelligence and Communication Networks, p.650-655.doi:10.1109/CICN.2010.128http://doi.org/10.1109/CICN.2010.128

Skorobogatov G,Barrado C,Salamí E,2020.Multiple UAV systems: a survey.Unmanned Syst,8(2):149-169.doi:10.1142/S2301385020500090http://doi.org/10.1142/S2301385020500090

Vig L,Adams JA,2006.Multi-robot coalition formation.IEEE Trans Robot,22(4):637-649.doi:10.1109/TRO.2006.878948http://doi.org/10.1109/TRO.2006.878948

Yang Y,Qiu XS,Meng LM,et al.,2014.Task coalition formation and self-adjustment in the wireless sensor networks.Int J Commun Syst,27(10):2241-2254.doi:10.1002/dac.2470http://doi.org/10.1002/dac.2470

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