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The Cooperative Hunters – Efficient and Scalable Drones Swarm for Multiple Targets Detection

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Swarms and Network Intelligence in Search

Part of the book series: Studies in Computational Intelligence ((SCI,volume 729))

Abstract

This work examines the Cooperative Hunters problem, where a swarm of Unmanned Air Vehicles (UAVs) is used for searching after one or more “evading targets”, which freely maneuver in a predefined area while trying to avoid detection by the swarm’s drones. By arranging themselves into an efficient geometric collaborative flight formation, the drones optimize their integrated sensing capabilities, enabling the completion of a successful search of a rectangular territory. This designed is shown to be able to guarantee the detection of the targets, even in cases where the targets are faster than the swarm’s drones and have better sensors. This is achieved through the inherent scalability of the proposed design which can compensate any addition to the targets’ ability to maneuver or foresee the behavior of the drones with an increase in the number of drones.

This chapter is based on work previously published in parts in [7, 9, 10].

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Notes

  1. 1.

    As in reality the sensors have finite detection time, the value of D used in the model can be slightly smaller than the actual sensors’ range, in order to generate positive overlap between a pair of UAVs moving in parallel (see more details in Sect. 5).

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Authors and Affiliations

  1. MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yaniv Altshuler & Alex Pentland

  2. Computer Science Department, Israeli Institute of Technology (Technion), Haifa, Israel

    Alfred M. Bruckstein

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  1. Yaniv Altshuler
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  2. Alex Pentland
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  3. Alfred M. Bruckstein
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Correspondence to Yaniv Altshuler .

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Altshuler, Y., Pentland, A., Bruckstein, A.M. (2018). The Cooperative Hunters – Efficient and Scalable Drones Swarm for Multiple Targets Detection. In: Swarms and Network Intelligence in Search. Studies in Computational Intelligence, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-319-63604-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-63604-7_7

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