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Categories, Quantum Computing, and Swarm Robotics: A Case Study

Author

Listed:
  • Maria Mannone

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy
    Department of Mathematics and Computer Sciences, University of Palermo, 90123 Palermo, Italy
    European Centre for Living Technology (ECLT), Dipartimento di Scienze Ambientali, Informatica e Statistica (DAIS), Ca’ Foscari University of Venice, 30172 Venice, Italy)

  • Valeria Seidita

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Antonio Chella

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy
    ICAR-CNR National Research Council, 90146 Palermo, Italy)

Abstract

The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement.

Suggested Citation

  • Maria Mannone & Valeria Seidita & Antonio Chella, 2022. "Categories, Quantum Computing, and Swarm Robotics: A Case Study," Mathematics, MDPI, vol. 10(3), pages 1-11, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:372-:d:734029
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    Citations

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    Cited by:

    1. Antonio Chella & Salvatore Gaglio & Giovanni Pilato & Filippo Vella & Salvatore Zammuto, 2022. "A Quantum Planner for Robot Motion," Mathematics, MDPI, vol. 10(14), pages 1-29, July.
    2. Yuri Tavares dos Passos & Xavier Duquesne & Leandro Soriano Marcolino, 2022. "On the Throughput of the Common Target Area for Robotic Swarm Strategies," Mathematics, MDPI, vol. 10(14), pages 1-38, July.

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