abstract

Improving energy efficiency based on behavioral model in a swarm of cooperative foraging robots

Authors:

Jong-Hyun Lee,

Chang Wook AhnAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 825 - 826

https://doi.org/10.1145/2001858.2002108

Published: 12 July 2011 Publication History

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Abstract

We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organization, robustness, and flexibility. However, high initial and operating cost are main barriers in operating multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behavior model regarding role division in concert with the search space division.

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C.W. Reynolds. Flocks, Herds, and Schools: A Distributed Behavioral Model. Computer Graphics, 21(4): 25--34, 1987.

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W. Liu, A. Winfield, J. Sa, J. Chen, L. Dou. Strategies for energy optimisation in a swarm of foraging robots, In: Sahin, E., Spears, W.M., Winfiel, A.F.T. (eds.) Swarm Robotics, Second International Workshop, LNCS 4433. Springer, Heidelberg, 2006.

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Alan F.T. Winfield. Foraging Robots, In: Meyers, R.A., (ed.) Encyclopedia of Complexity and Systems Science. Springer, New York, pages 3682--3700, 2009.

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N. Trawny, S.I. Roumeliotis, G.B. Giannakis. Cooperative multi-robot localization under communication constraints. 2009 International Conference on Robotics and Automation (ICRA09). Kobe, Japan, pp. 4394--4400, 2009.

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Pandey ABanerjee S(2021)Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GAResearch Anthology on Recent Trends, Tools, and Implications of Computer Programming10.4018/978-1-7998-3016-0.ch007(133-150)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3016-0.ch007
Pandey ABanerjee S(2018)Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GAInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.20180701059:3(88-104)Online publication date: Jul-2018
https://doi.org/10.4018/IJAMC.2018070105

Index Terms

Improving energy efficiency based on behavioral model in a swarm of cooperative foraging robots
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Pandey ABanerjee S(2021)Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GAResearch Anthology on Recent Trends, Tools, and Implications of Computer Programming10.4018/978-1-7998-3016-0.ch007(133-150)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3016-0.ch007
Pandey ABanerjee S(2018)Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GAInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.20180701059:3(88-104)Online publication date: Jul-2018
https://doi.org/10.4018/IJAMC.2018070105

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Foraging swarm robots system adopting honey bee swarm for improving energy efficiency

Bee-inspired foraging in an embodied swarm

Swarm-inspired Algorithms for Prioritized Foraging

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