CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-11-16
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Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0001-9354-6974
Chen CHEN, Xiaochen WU, Jie CHEN, Panos M. PARDALOS, Shuxin DING. Dynamic grouping of heterogeneous agents for exploration and strike missions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 86-100.
@article{title="Dynamic grouping of heterogeneous agents for exploration and strike missions",
author="Chen CHEN, Xiaochen WU, Jie CHEN, Panos M. PARDALOS, Shuxin DING",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="1",
pages="86-100",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000352"
}
%0 Journal Article
%T Dynamic grouping of heterogeneous agents for exploration and strike missions
%A Chen CHEN
%A Xiaochen WU
%A Jie CHEN
%A Panos M. PARDALOS
%A Shuxin DING
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 1
%P 86-100
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000352
TY - JOUR
T1 - Dynamic grouping of heterogeneous agents for exploration and strike missions
A1 - Chen CHEN
A1 - Xiaochen WU
A1 - Jie CHEN
A1 - Panos M. PARDALOS
A1 - Shuxin DING
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 1
SP - 86
EP - 100
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000352
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.
[1]Butenko S, Murphey R, Pardalos PM, 2003. Cooperative Control: Models, Applications and Algorithms. Springer, Dordrecht, the Netherlands.
[2]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.2004.830916
[3]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.
[4]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: 10.2514/6.2010-8422
[5]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: 10.1177/0278364904045564
[6]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.2020.100686
[7]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: 10.1007/978-3-540-88063-9
[8]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).
[9]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.2019.100769
[10]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/S2301385019500055
[11]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/s10846-013-9887-6
[12]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: 10.1007/978-3-540-74356-9
[13]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.2013.51
[14]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/s10846-010-9439-2
[15]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.2014.6911384
[16]Moritz RLV, Middendorf M, 2015. Decentralized and dynamic group formation of reconfigurable agents. Memet Comput, 7(2):77-91. doi: 10.1007/s12293-014-0149-3
[17]Murphey R, Pardalos PM, 2002. Cooperative Control and Optimization. Springer, Boston, USA. doi: 10.1007/b130435
[18]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.2015.7151319
[19]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/S0219686719500264
[20]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_24
[21]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.2016.2524405
[22]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.2015.7219891
[23]Pardalos PM, Grundel D, Murphey R, et al., 2008. Cooperative Networks: Control and Optimization. Edward Elgar Publishing, Cheltenham, UK.
[24]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: 10.5555/1838186.1838191
[25]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)00045-9
[26]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.2010.128
[27]Skorobogatov G, Barrado C, Salamí E, 2020. Multiple UAV systems: a survey. Unmanned Syst, 8(2):149-169. doi: 10.1142/S2301385020500090
[28]Vig L, Adams JA, 2006. Multi-robot coalition formation. IEEE Trans Robot, 22(4):637-649. doi: 10.1109/TRO.2006.878948
[29]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.2470
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