A unifying method-based classification of robot swarm spatial self-organisation behaviours

被引:3
作者
Henard, Aymeric [1 ]
Riviere, Jeremy [1 ]
Peillard, Etienne [2 ]
Kubicki, Sebastien [3 ]
Coppin, Gilles [2 ]
机构
[1] Univ Brest, Lab STICC, CNRS, 20 Ave Victor le Gorgeu, F-29200 Brest, France
[2] IMT Atlantique, Lab STICC, CNRS, Brest, France
[3] ENIB, Lab STICC, CNRS, Brest, France
关键词
Swarm intelligence; robot swarm; self-organisation; aggregation; flocking; coverage; pattern formation; shape formation; PRESERVING FLOCKING ALGORITHM; PATTERN-FORMATION; AGGREGATION BEHAVIOR; COLLECTIVE BEHAVIOR; SYSTEMS; GENERATION; STRATEGIES; EMERGENCE; DYNAMICS; COVERAGE;
D O I
10.1177/10597123231163948
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Self-organisation in robot swarms can produce collective behaviours, particularly through spatial self-organisation. For example, it can be used to ensure that the robots in a swarm move collectively. However, from a designer's point of view, understanding precisely what happens in a swarm that allows these behaviours to emerge at the macroscopic level remains a difficult task. The same behaviour can come from multiple different controllers (ie the control algorithm of a robot) and a single controller can give rise to multiple different behaviours, sometimes caused by slight changes in self-organisation. To grasp the causes of these differences, it is necessary to investigate the relationships between the many methods of self-organisation that exist and the various behaviours that can be obtained. The work presented here addresses self-organisation in robot swarms by focusing on the main behaviours that lead to spatial self-organisation of the robots. First, we propose a unified definition of the different behaviours and present an original classification system highlighting ten self-organisation methods that each allow one or more behaviours to be performed. An analysis, based on this classification system, links the identified mechanisms with behaviours that could be considered as obtainable or not. Finally, we discuss some perspectives on this work, notably from the point of view of an operator or designer.
引用
收藏
页码:577 / 599
页数:23
相关论文
共 141 条
[1]   Animal aggregations [J].
Allee, WC .
QUARTERLY REVIEW OF BIOLOGY, 1927, 2 (03) :367-398
[2]  
Alonso-Mora J., 2011, 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), P4512, DOI 10.1109/ICRA.2011.5980269
[3]  
[Anonymous], 1998, Netlogo Flocking Model. Center for Connected Learning and Computer-based Modeling
[4]  
[Anonymous], IEEE 2007 INT C ROB
[5]  
AOKI I, 1982, B JPN SOC SCI FISH, V48, P1081
[6]  
Arvin F, 2018, IEEE INT C INT ROBOT, P4288, DOI 10.1109/IROS.2018.8593961
[7]   Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method [J].
Arvin, Farshad ;
Turgut, Ali Emre ;
Bazyari, Farhad ;
Arikan, Kutluk Bilge ;
Bellotto, Nicola ;
Yue, Shigang .
ADAPTIVE BEHAVIOR, 2014, 22 (03) :189-206
[8]  
AURENHAMMER F, 1991, COMPUT SURV, V23, P345, DOI 10.1145/116873.116880
[9]   Drone Deep Reinforcement Learning: A Review [J].
Azar, Ahmad Taher ;
Koubaa, Anis ;
Ali Mohamed, Nada ;
Ibrahim, Habiba A. ;
Ibrahim, Zahra Fathy ;
Kazim, Muhammad ;
Ammar, Adel ;
Benjdira, Bilel ;
Khamis, Alaa M. ;
Hameed, Ibrahim A. ;
Casalino, Gabriella .
ELECTRONICS, 2021, 10 (09)
[10]   Composable continuous-space programs for robotic swarms [J].
Bachrach, Jonathan ;
Beal, Jacob ;
McLurkin, James .
NEURAL COMPUTING & APPLICATIONS, 2010, 19 (06) :825-847