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Distributed Task Allocation in Swarms of Robots

Distributed Task Allocation in Swarms of Robots

Aleksandar Jevtic, Diego Andina, Mo Jamshidi
Copyright: © 2013 |Pages: 24
ISBN13: 9781466626669|ISBN10: 1466626666|EISBN13: 9781466626973
DOI: 10.4018/978-1-4666-2666-9.ch009
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MLA

Jevtic, Aleksandar, et al. "Distributed Task Allocation in Swarms of Robots." Swarm Intelligence for Electric and Electronic Engineering, edited by Girolamo Fornarelli and Luciano Mescia, IGI Global, 2013, pp. 170-193. https://doi.org/10.4018/978-1-4666-2666-9.ch009

APA

Jevtic, A., Andina, D., & Jamshidi, M. (2013). Distributed Task Allocation in Swarms of Robots. In G. Fornarelli & L. Mescia (Eds.), Swarm Intelligence for Electric and Electronic Engineering (pp. 170-193). IGI Global. https://doi.org/10.4018/978-1-4666-2666-9.ch009

Chicago

Jevtic, Aleksandar, Diego Andina, and Mo Jamshidi. "Distributed Task Allocation in Swarms of Robots." In Swarm Intelligence for Electric and Electronic Engineering, edited by Girolamo Fornarelli and Luciano Mescia, 170-193. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2666-9.ch009

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Abstract

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

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