基于统一方法的机器人群体空间自组织行为分类

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Adaptive Behavior Pub Date : 2023-03-23 DOI:10.1177/10597123231163948
Aymeric Hénard, Jérémy Rivière, Etienne Peillard, Sébastien Kubicki, Gilles Coppin
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引用次数: 0

摘要

机器人群中的自组织可以产生集体行为,特别是通过空间自组织。例如,它可以用来确保群体中的机器人集体移动。然而,从设计师的角度来看,准确理解群体中发生了什么,使这些行为能够在宏观层面出现,仍然是一项艰巨的任务。相同的行为可以来自多个不同的控制器(即机器人的控制算法),单个控制器可以产生多种不同的行为,有时是由自组织的微小变化引起的。为了理解这些差异的原因,有必要调查现有的许多自我组织方法与可以获得的各种行为之间的关系。这里介绍的工作通过关注导致机器人空间自组织的主要行为来解决机器人群中的自组织问题。首先,我们提出了不同行为的统一定义,并提出了一个原始的分类系统,突出了十种自我组织方法,每种方法都允许执行一种或多种行为。基于这一分类系统的分析将所确定的机制与可被认为是可获得的或不可获得的行为联系起来。最后,我们讨论了对这项工作的一些看法,特别是从操作员或设计师的角度。
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A unifying method-based classification of robot swarm spatial self-organisation behaviours
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.
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来源期刊
Adaptive Behavior
Adaptive Behavior 工程技术-计算机:人工智能
CiteScore
4.30
自引率
18.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: _Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling. Print ISSN: 1059-7123
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