Evolving collective behaviours in simulated kilobots

Jane Holland; Josephine Griffith; Colm O'Riordan

doi:10.13025/18864

Evolving collective behaviours in simulated kilobots

Computer Science

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

The field of Evolutionary Robotics has multiple common tasks and widely used benchmark activities such as navigation, obstacle avoidance, and phototaxis. We present an evolutionary approach to learning behaviours that demonstrate emergent collective phototaxis in a swarm of simulated robots. Our approach demonstrates that evolutionary computation can be used to evolve the emergent, self-organising behaviours of clustering and phototaxis in a population of simulated robots where the robots possess limited capabilities. In addition to demonstrating the feasibility of the approach, we show that the evolved behaviours are also robust to noise and flexible in changing environments.

Original language	English
Title of host publication	Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Publisher	Association for Computing Machinery
Pages	824-831
Number of pages	8
ISBN (Electronic)	9781450351911
DOIs	https://doi.org/10.13025/18864 https://doi.org/10.1145/3167132.3167223
Publication status	Published - 9 Apr 2018
Event	33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France Duration: 9 Apr 2018 → 13 Apr 2018

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	33rd Annual ACM Symposium on Applied Computing, SAC 2018
Country/Territory	France
City	Pau
Period	9/04/18 → 13/04/18

Keywords

Collective behaviour
Evolutionary swarm robotics
Genetic algorithms
Kilobots
Phototaxis

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10.13025/18864Licence: CC BY-NC-ND
10.1145/3167132.3167223

Evolving Collective Behaviours in Simulated aKilobots SACAccepted author manuscript, 1.55 MBLicence: CC BY-NC-ND

Cite this

@inproceedings{201f9a9541da4f2c90ac5b33298d9b0e,

title = "Evolving collective behaviours in simulated kilobots",

abstract = "The field of Evolutionary Robotics has multiple common tasks and widely used benchmark activities such as navigation, obstacle avoidance, and phototaxis. We present an evolutionary approach to learning behaviours that demonstrate emergent collective phototaxis in a swarm of simulated robots. Our approach demonstrates that evolutionary computation can be used to evolve the emergent, self-organising behaviours of clustering and phototaxis in a population of simulated robots where the robots possess limited capabilities. In addition to demonstrating the feasibility of the approach, we show that the evolved behaviours are also robust to noise and flexible in changing environments.",

keywords = "Collective behaviour, Evolutionary swarm robotics, Genetic algorithms, Kilobots, Phototaxis",

author = "Jane Holland and Josephine Griffith and Colm O'Riordan",

note = "Publisher Copyright: {\textcopyright} 2018 ACM.; 33rd Annual ACM Symposium on Applied Computing, SAC 2018 ; Conference date: 09-04-2018 Through 13-04-2018",

year = "2018",

month = apr,

day = "9",

doi = "10.13025/18864",

language = "English",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = " Association for Computing Machinery",

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booktitle = "Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018",

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Holland, J, Griffith, J & O'Riordan, C 2018, Evolving collective behaviours in simulated kilobots. in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, pp. 824-831, 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, 9/04/18. https://doi.org/10.13025/18864, https://doi.org/10.1145/3167132.3167223

Evolving collective behaviours in simulated kilobots. / Holland, Jane; Griffith, Josephine ; O'Riordan, Colm.
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018. Association for Computing Machinery, 2018. p. 824-831 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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T1 - Evolving collective behaviours in simulated kilobots

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AU - O'Riordan, Colm

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N2 - The field of Evolutionary Robotics has multiple common tasks and widely used benchmark activities such as navigation, obstacle avoidance, and phototaxis. We present an evolutionary approach to learning behaviours that demonstrate emergent collective phototaxis in a swarm of simulated robots. Our approach demonstrates that evolutionary computation can be used to evolve the emergent, self-organising behaviours of clustering and phototaxis in a population of simulated robots where the robots possess limited capabilities. In addition to demonstrating the feasibility of the approach, we show that the evolved behaviours are also robust to noise and flexible in changing environments.

AB - The field of Evolutionary Robotics has multiple common tasks and widely used benchmark activities such as navigation, obstacle avoidance, and phototaxis. We present an evolutionary approach to learning behaviours that demonstrate emergent collective phototaxis in a swarm of simulated robots. Our approach demonstrates that evolutionary computation can be used to evolve the emergent, self-organising behaviours of clustering and phototaxis in a population of simulated robots where the robots possess limited capabilities. In addition to demonstrating the feasibility of the approach, we show that the evolved behaviours are also robust to noise and flexible in changing environments.

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KW - Evolutionary swarm robotics

KW - Genetic algorithms

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KW - Phototaxis

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BT - Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018

PB - Association for Computing Machinery

T2 - 33rd Annual ACM Symposium on Applied Computing, SAC 2018

Y2 - 9 April 2018 through 13 April 2018

ER -

Evolving collective behaviours in simulated kilobots

Abstract

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Conference

Keywords

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