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Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs

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Swarms and Network Intelligence in Search

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

Abstract

Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator. Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same — how can its detection probability be maximized using as little number of drones as possible. In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones — capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat’s potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks. We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.

This work was previously published in [20].

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Notes

  1. 1.

    Our model assumes continuous selection of drones’ types. In reality of course there is a finite number of drones model. Therefore, after producing the optimal value for the cost of a single drone, we would select the two available models closest in cost to this optimal value (namely, the more expensive cheaper one, and the cheapest more expensive one), assign their cost in the model, and select the one for which the merit function is higher.

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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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Correspondence to Yaniv Altshuler .

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Altshuler, Y., Pentland, A., Bruckstein, A.M. (2018). Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs. 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_8

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