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Tasks Allocation for Rescue Robotics: A Replicator Dynamics Approach

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

Tasks allocation on homogeneous rescue robots has been an important research field in recent years due to the advance in both robotics and artificial intelligence. Nevertheless, catastrophic scenarios still represent a hard challenge because of the complexity and uncertainty of their characteristics and parameters which produce highly heterogeneous tasks as a result of the different nature of problems they are intended for. We propose hereby an approach to the catastrophic condition exposed above by solving the replicator dynamics equation to reduce the effects of uncertainty. A standard metric based on tasks progress is defined and the main elements of game theory like payoff matrix and allocation ratios are computed in order to obtain the number of robots assigned to each task. Finally, software was built for simulation; by using this software some scenarios were defined and simulations were run to compare and validate our approach.

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Author information

Authors and Affiliations

  1. Santo Tomás University, Bogotá, Colombia

    Sindy Amaya & Armando Mateus

Authors
  1. Sindy Amaya
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  2. Armando Mateus
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Corresponding authors

Correspondence to Sindy Amaya or Armando Mateus .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Amaya, S., Mateus, A. (2019). Tasks Allocation for Rescue Robotics: A Replicator Dynamics Approach. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_54

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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