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Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles

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Ad-Hoc, Mobile, and Wireless Networks (ADHOC-NOW 2020)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12338))

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Abstract

Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms.

This work received funding from the Italian University and Research Ministry under project RAFAEL (National Research Program, contract No. ARS01_00305) and from the European Union’s Horizon 2020 programme under project Comp4Drones (ECSEL Joint Undertaking RIA-2018, grant agreement No. 826610).

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Author information

Authors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy

    Raffaele Carli, Graziana Cavone, Paolo Scarabaggio & Mariagrazia Dotoli

  2. Center of Excellence DEWS, Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy

    Nicola Epicoco & Mario Di Ferdinando

Authors
  1. Raffaele Carli
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  2. Graziana Cavone
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  3. Nicola Epicoco
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  4. Mario Di Ferdinando
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  5. Paolo Scarabaggio
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  6. Mariagrazia Dotoli
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Corresponding author

Correspondence to Nicola Epicoco .

Editor information

Editors and Affiliations

  1. DEI, Polytechnic University of Bari, Bari, Italy

    Luigi Alfredo Grieco

  2. DEI, Polytechnic University of Bari, Bari, Italy

    Gennaro Boggia

  3. DEI, Polytechnic University of Bari, Bari, Italy

    Giuseppe Piro

  4. Duquesne University, Pittsburgh, PA, USA

    Yaser Jararweh

  5. Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Claudia Campolo

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Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P., Dotoli, M. (2020). Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. In: Grieco, L.A., Boggia, G., Piro, G., Jararweh, Y., Campolo, C. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science(), vol 12338. Springer, Cham. https://doi.org/10.1007/978-3-030-61746-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-61746-2_7

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  • Online ISBN: 978-3-030-61746-2

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