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Balancing exploitation of renewable resources by a robot swarm

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Abstract

Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation effort is correctly balanced is particularly challenging. Additionally, the possibility to exploit resources by multiple robots opens the way to focusing the effort either on a single or on multiple resources in parallel. This means that the swarm needs to collectively decide whether to remain cohesive or split among multiple resources, as a function of the ability of the available resources to replenish after exploitation. In this paper, we propose a decentralised strategy for a swarm of robots that adapts to the available resources and balances the effort among them, hence allowing to maximise the exploitation rate while avoiding to completely deplete the resources. A detailed analysis of the strategy parameters provides insights into the working principles and expected performance of the robot swarm.

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Acknowledgements

Vito Trianni acknowledges the support by the European Commission FP7 Programme People: Marie-Curie Actions through the project “DICE, Distributed Cognition Engineering” (Grant Agreement Number 631297). Marco Dorigo acknowledges the support from the Belgian F.R.S.-FNRS, of which he is a Research Director.

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Roman Miletitch & Marco Dorigo

  2. ISTC, Italian National Research Council, Rome, Italy

    Vito Trianni

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  1. Roman Miletitch
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  2. Marco Dorigo
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  3. Vito Trianni
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Correspondence to Vito Trianni.

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Guest editor: Roderich Groß.

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Miletitch, R., Dorigo, M. & Trianni, V. Balancing exploitation of renewable resources by a robot swarm. Swarm Intell 12, 307–326 (2018). https://doi.org/10.1007/s11721-018-0159-8

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  • DOI: https://doi.org/10.1007/s11721-018-0159-8

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