Power-law distribution of long-term experimental data in swarm robotics

Farshad Arvin; Abdolrahman Attar; Ali Emre Turgut; Shigang Yue

doi:10.1007/978-3-319-20466-6_58

Power-law distribution of long-term experimental data in swarm robotics

Farshad Arvin, Abdolrahman Attar, Ali Emre Turgut, Shigang Yue

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Nature
Pages	551-559
Number of pages	9
Volume	9140
ISBN (Print)	9783319204659
DOIs	https://doi.org/10.1007/978-3-319-20466-6_58
Publication status	Published - 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9140

Keywords

Aggregation
Modelling
Power-law distribution
Swarm robotics

Access to Document

10.1007/978-3-319-20466-6_58

http://www.mendeley.com/research/powerlaw-distribution-longterm-experimental-data-swarm-robotics

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Arvin, F., Attar, A., Turgut, A. E., & Yue, S. (2015). Power-law distribution of long-term experimental data in swarm robotics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9140, pp. 551-559). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9140). Springer Nature. https://doi.org/10.1007/978-3-319-20466-6_58

Arvin, Farshad ; Attar, Abdolrahman ; Turgut, Ali Emre et al. / Power-law distribution of long-term experimental data in swarm robotics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9140 Springer Nature, 2015. pp. 551-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.",

keywords = "Aggregation, Modelling, Power-law distribution, Swarm robotics",

author = "Farshad Arvin and Abdolrahman Attar and Turgut, {Ali Emre} and Shigang Yue",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Arvin, F, Attar, A, Turgut, AE & Yue, S 2015, Power-law distribution of long-term experimental data in swarm robotics. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9140, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9140, Springer Nature, pp. 551-559. https://doi.org/10.1007/978-3-319-20466-6_58

Power-law distribution of long-term experimental data in swarm robotics. / Arvin, Farshad; Attar, Abdolrahman; Turgut, Ali Emre et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9140 Springer Nature, 2015. p. 551-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9140).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Power-law distribution of long-term experimental data in swarm robotics

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AU - Turgut, Ali Emre

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AB - Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.

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KW - Modelling

KW - Power-law distribution

KW - Swarm robotics

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DO - 10.1007/978-3-319-20466-6_58

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SN - 9783319204659

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Arvin F, Attar A, Turgut AE, Yue S. Power-law distribution of long-term experimental data in swarm robotics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9140. Springer Nature. 2015. p. 551-559. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-20466-6_58

Power-law distribution of long-term experimental data in swarm robotics

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