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Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc clouds

Author

Listed:
  • Leandro R. Costa

    (Polytechnique Montréal)

  • Daniel Aloise

    (Polytechnique Montréal)

  • Luca G. Gianoli

    (Humanitas Solutions)

  • Andrea Lodi

    (Polytechnique Montréal)

Abstract

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors—also known as swarming behaviors—within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members. We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.

Suggested Citation

  • Leandro R. Costa & Daniel Aloise & Luca G. Gianoli & Andrea Lodi, 2022. "Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc clouds," Journal of Heuristics, Springer, vol. 28(4), pages 539-582, August.
    • Handle: RePEc:spr:joheur:v:28:y:2022:i:4:d:10.1007_s10732-022-09502-7
    DOI: 10.1007/s10732-022-09502-7
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    References listed on IDEAS

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    1. Caraballo, L.E. & Díaz-Báñez, J.M. & Maza, I. & Ollero, A., 2017. "The block-information-sharing strategy for task allocation: A case study for structure assembly with aerial robots," European Journal of Operational Research, Elsevier, vol. 260(2), pages 725-738.
    2. Xiaoting Ji & Xiangke Wang & Yifeng Niu & Lincheng Shen, 2015. "Cooperative Search by Multiple Unmanned Aerial Vehicles in a Nonconvex Environment," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-19, August.
    3. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    4. Hyondong Oh & Seungkeun Kim & Antonios Tsourdos & Brian A. White, 2014. "Coordinated road-network search route planning by a team of UAVs," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(5), pages 825-840, May.
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