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A Bio-inspired Aggregation with Robot Swarm Using Real and Simulated Mobile Robots

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Towards Autonomous Robotic Systems (TAROS 2018)

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Abstract

This paper presents an implementation of a bio-inspired aggregation scenario using swarm robots. The aggregation scenario took inspiration from honeybee’s thermotactic behaviour in finding an optimal zone in their comb. To realisation of the aggregation scenario, real and simulated robots with different population sizes were used. Mona, which is an open-source and open-hardware platform was deployed to play the honeybee’s role in this scenario. A model of Mona was also generated in Stage for simulation of aggregation scenario with large number of robots. The results of aggregation with real- and simulated-robots showed reliable aggregations and a population dependent swarm performance. Moreover, the results demonstrated a direct correlation between the results observed from the real robot and simulation experiments.

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Acknowledgement

This work was supported by the EPSRC (Project No. EP/P01366X/1).

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Authors and Affiliations

  1. Robotics for Extreme Environments Lab (REEL), School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Sarika Ramroop, Farshad Arvin, Simon Watson, Joaquin Carrasco-Gomez & Barry Lennox

Authors
  1. Sarika Ramroop
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  2. Farshad Arvin
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  3. Simon Watson
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  4. Joaquin Carrasco-Gomez
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  5. Barry Lennox
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Corresponding author

Correspondence to Farshad Arvin .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, United Kingdom

    Manuel Giuliani

  2. University of Bath, Bath, United Kingdom

    Tareq Assaf

  3. University of Bristol, Bristol, United Kingdom

    Maria Elena Giannaccini

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Ramroop, S., Arvin, F., Watson, S., Carrasco-Gomez, J., Lennox, B. (2018). A Bio-inspired Aggregation with Robot Swarm Using Real and Simulated Mobile Robots. In: Giuliani, M., Assaf, T., Giannaccini, M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science(), vol 10965. Springer, Cham. https://doi.org/10.1007/978-3-319-96728-8_27

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  • DOI: https://doi.org/10.1007/978-3-319-96728-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96727-1

  • Online ISBN: 978-3-319-96728-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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