Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

被引:23
作者
Wu, Gongxing [1 ,2 ]
Xu, Taotao [3 ]
Sun, Yushan [2 ]
Zhang, Jiawei [2 ]
机构
[1] Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai, Peoples R China
[2] Harbin Engn Univ, Sci & Technol Underwater Vehicle Lab, Harbin, Peoples R China
[3] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai, Peoples R China
来源
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | 2022年 / 19卷 / 02期
基金
中国国家自然科学基金;
关键词
Swarm intelligence; cooperative searching; swarm hunting; unmanned surface vessel; multi-robot system; BEE COLONY ALGORITHM; COOPERATIVE CONTROL; COLLISION-AVOIDANCE; TASK ALLOCATION; VEHICLES; BEHAVIOR; SYSTEMS;
D O I
10.1177/17298806221091885
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.
引用
收藏
页数:20
相关论文
共 101 条
  • [1] An WG., THESIS NW POLYTECHNI
  • [2] Crowding population-based ant colony optimisation for the multi-objective travelling salesman problem
    Angus, Daniel
    [J]. 2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN MULTI-CRITERIA DECISION MAKING, 2007, : 333 - 340
  • [3] [Anonymous], 2003, Ph.D. thesis
  • [4] Guest editorial - Advances in multirobot systems
    Arai, T
    Pagello, E
    Parker, LE
    [J]. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 2002, 18 (05): : 655 - 661
  • [5] Asama H., IEEE RSJ INT WORKSH, P283
  • [6] Improved quick artificial bee colony (iqABC) algorithm for global optimization
    Aslan, Selcuk
    Badem, Hasan
    Karaboga, Dervis
    [J]. SOFT COMPUTING, 2019, 23 (24) : 13161 - 13182
  • [7] Benda M., OPTIMAL COOPERATION
  • [8] Beni G., 1993, ROBOTS BIOLOGICAL SY, P703, DOI [DOI 10.1007/978-3-642-58069-7_38, 10.1007/978-3-642-58069-7_38]
  • [9] Bourgault F, 2002, 2002 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-3, PROCEEDINGS, P540, DOI 10.1109/IRDS.2002.1041446
  • [10] [曹勇 Cao Yong], 2019, [复杂系统与复杂性科学, Complex Systems and Complexity Science], V16, P1