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Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions

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  • Jose Guerrero
  • Gabriel Oliver
  • Oscar Valero

Abstract

Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time.

Suggested Citation

  • Jose Guerrero & Gabriel Oliver & Oscar Valero, 2017. "Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-26, January.
    • Handle: RePEc:plo:pone00:0170659
    DOI: 10.1371/journal.pone.0170659
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    Cited by:

    1. Maria-del-Mar Bibiloni-Femenias & José Guerrero & Juan-José Miñana & Oscar Valero, 2021. "Indistinguishability Operators via Yager t -norms and Their Applications to Swarm Multi-Agent Task Allocation," Mathematics, MDPI, vol. 9(2), pages 1-21, January.
    2. Xin Zhou & Weiping Wang & Tao Wang & Xiaobo Li & Tian Jing, 2018. "Continuous patrolling in uncertain environment with the UAV swarm," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-29, August.

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