Algorithm for swarm robot flocking behavior

Li, Xiang; Ercan, M. Fikret; Zhou, Yi; Fung, Yu-Fai

doi:10.1109/icara.2000.4803943

Cited by 5 publications

(9 citation statements)

References 6 publications

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“…Mao Yang et al [18] suggested a distributed co-adaptive control algorithm and they proved that all swarm members can converge to a common velocity only by means of local information. Li Xiang et al [19] present a decentralized control algorithm for a swarm of robots based on geometric approach. They developed a control algorithm, which was executed by all members of a swarm to achieve collective behavior.…”

Section: Coordination and Control Techniquesmentioning

confidence: 99%

A review of research in multi-robot systems

Gautam

Mohan

2012

2012 IEEE 7th International Conference on Industrial and Information Systems (ICIIS)

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Formally, a collection of two or more autonomous mobile robots working together are termed as teams or societies of mobile robots. In multi robot systems simple robots are allowed to coordinate with each other to achieve some well defined goals. In these kinds of systems robots are far less capable as an entity, but the real power lies in cooperation of multiple robots. The simplicity of multi-robots have produced a potentially wide set of applications such as military missions (battlefield surveillance), searching for survivors in disaster hit areas, parallel and simultaneous transportation of vehicles, and delivery of payloads. Although the research on multi-robot systems has attracted considerable attention worldwide in the past decade, the research in this area is still in its infancy. This paper surveys various interaction techniques in multi robot systems which are important with respect to goal attainment and task completion.

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Section: Coordination and Control Techniquesmentioning

confidence: 99%

A review of research in multi-robot systems

Gautam

Mohan

2012

2012 IEEE 7th International Conference on Industrial and Information Systems (ICIIS)

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“…However, the term 'flocking' is still mostly used in swarm intelligence (e.g. Hanada et al, 2007;Su et al, 2009;Xiang et al, 2009;Liu et al, 2021b;Bezcioglu et al, 2021), and represents the collective motion behaviours of agents.…”

Section: Flockingmentioning

confidence: 99%

“…ø (Ludwig & Gini, 2006;Hoff et al, 2010;Bayert & Khorbotly, 2019;Payton et al, 2001) (Rubenstein et al, 2014;Wang & Zhang, 2021;Mamei et al, 2004;Støy, 2003) Relative position ? (Ge & Fua, 2005;Hanada et al, 2007;Xiang et al, 2009) ? (Hanada et al, 2007;Xiang et al, 2009;Desai et al, 2001;Shiell & Vardy, 2016;Poulton et al, 2004;Balch & Arkin, 1998;G üzel et al, 2017;Li et al, 2017;Li et al, 2019;Coppola et al, 2019) Stigmergy ?…”

Section: Machine Learningmentioning

confidence: 99%

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

et al. 2023

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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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“…Robotic swarms have the potential to transform how we complete tasks from parcel delivery to tending to rooftop gardens. Previous work shows that robotic swarms can act like flocks of birds or schools of fish through decentralized algorithms [1]. Although this flocking behavior enables swarms to move and congregate, sometimes more specific movement is necessary.…”

Section: Applications Swarm Behaviormentioning

confidence: 99%

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Cooley

Wolf

Borowczak

2018

2018 IEEE International Smart Cities Conference (ISC2)

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Unmanned Aerial Vehicles (UAVs), referenced as drones, have advanced to consumer adoption for hobby and business use. Drone applications, such as infrastructure technology, security mechanisms, and resource delivery, are just the starting point. More complex tasks are possible through the use of UAV swarms. These tasks increase the potential impacts that drones will have on smart cities, modern cities which have fully adopted technology in order to enhance daily operations as well as the welfare of it's citizens. Smart cities not only consist of static mesh networks of sensors, but can contain dynamic aspects as well including both ground and air based autonomous vehicles.Networked computational devices require paramount security to ensure the safety of a city. To accomplish such high levels of security, services rely on secure-by-design protocols, impervious to security threats. Given the large number of sensors, autonomous vehicles, and other advancements, smart cities necessitates this level of security. The SHARK protocol (Secure, Heterogeneous, Autonomous, and Rotational Knowledge for Swarms) ensures this kind of security by allowing for new applications for UAV swarm technology. Enabling drones to circle a target without a centralized control or selecting lead agents, the SHARKS protocol performs organized movement among agents without creating a central point for attackers to target. Through comparisons on the stability of the protocol in different settings, experiments demonstrate the efficiency and capacity of the SHARKS protocol.

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Algorithm for swarm robot flocking behavior

Cited by 5 publications

References 6 publications

A review of research in multi-robot systems

A review of research in multi-robot systems

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

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

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