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The block-information-sharing strategy for task allocation: A case study for structure assembly with aerial robots

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  • Caraballo, L.E.
  • Díaz-Báñez, J.M.
  • Maza, I.
  • Ollero, A.

Abstract

A new paradigm for task allocation in cooperative multi-robot systems is proposed in this paper. The block-information-sharing (BIS) strategy is a fully distributed approach, where robots dynamically allocate their tasks following the principle of share & divide to maintain an optimal allocation according to their capabilities. Prior studies on multi-robot information sharing strategies do not formally address the proof of convergence to the optimal allocation, nor its robustness to dynamic changes in the execution of the global task. The BIS strategy is introduced in a general framework and the convergence to the optimal allocation is theoretically proved. As an illustration of the approach, the strategy is applied to the automatic construction of truss structures with aerial robots. In order to demonstrate the benefits of the strategy, algorithms and simulations are presented for a team of heterogeneous robots that can dynamically reallocate tasks during the execution of a mission.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:260:y:2017:i:2:p:725-738
    DOI: 10.1016/j.ejor.2016.12.049
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    References listed on IDEAS

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    1. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2007. "A classification of assembly line balancing problems," European Journal of Operational Research, Elsevier, vol. 183(2), pages 674-693, December.
    2. Scholl, Armin & Becker, Christian, 2006. "State-of-the-art exact and heuristic solution procedures for simple assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 666-693, February.
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    Cited by:

    1. Rave, Alexander & Fontaine, Pirmin & Kuhn, Heinrich, 2023. "Drone location and vehicle fleet planning with trucks and aerial drones," European Journal of Operational Research, Elsevier, vol. 308(1), pages 113-130.
    2. 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.
    3. Caraballo, Luis Evaristo & Díaz-Báñez, José-Miguel & Kroher, Nadine, 2021. "A polynomial algorithm for balanced clustering via graph partitioning," European Journal of Operational Research, Elsevier, vol. 289(2), pages 456-469.

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