Abstract
One of the key tasks of autonomous mobile robots is to explore the unknown environment under limited energy and deadline conditions. In this paper, we focus on one of the most efficient random walks found in the natural and biological system, i.e., Lévy walk. We show how Lévy properties disappear in larger robot swarm sizes because of spatial interferences and propose a novel behavioral algorithm to preserve Lévy properties at the collective level. Our initial findings hold potential to accelerate target search processes in large unknown environments by parallelizing Lévy exploration using a group of robots.
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The ARGoS simulator allows to simulate large swarms of robots while taking the desired level of physical details into consideration.
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Khaluf, Y., Van Havermaet, S., Simoens, P. (2018). Collective Lévy Walk for Efficient Exploration in Unknown Environments. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_24
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DOI: https://doi.org/10.1007/978-3-319-99344-7_24
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