10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Research Article

A cellular model of swarm intelligence in bees and robots

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  • @INPROCEEDINGS{10.4108/eai.22-3-2017.152396,
        author={Martina Szopek and Martin Stefanec and Michael Bodi and Gerald Radspieler and Thomas Schmickl},
        title={A cellular model of swarm intelligence in bees and robots},
        proceedings={10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)},
        publisher={ACM},
        proceedings_a={BICT},
        year={2017},
        month={3},
        keywords={honeybees cellular model collective decision making bio-hybrid system mixed society robots},
        doi={10.4108/eai.22-3-2017.152396}
    }
    
  • Martina Szopek
    Martin Stefanec
    Michael Bodi
    Gerald Radspieler
    Thomas Schmickl
    Year: 2017
    A cellular model of swarm intelligence in bees and robots
    BICT
    ACM
    DOI: 10.4108/eai.22-3-2017.152396
Martina Szopek1,*, Martin Stefanec1, Michael Bodi1, Gerald Radspieler1, Thomas Schmickl1
  • 1: Artificial Life Lab of the Department of Zoology Karl-Franzens University Graz, Austria
*Contact email: martina.szopek@uni-graz.at

Abstract

We present here a simple cellular model of random motion and social interaction of young honeybees making swarm intelligent decisions in complex dynamic temperature fields. We model also behaviors of stationary robots that affect those bees. Our study looks for a first as-simple-as-possible approach towards modeling such a bio-hybrid system. Our model predicts observed collective behaviors qualitatively very well by modeling a correlated random walk and a simple social interaction mechanism. We found that even a very simple 2-dimensional cellular model with a limited state space of 16 bit per cell suffices. Ultimately, the simplicity of the model allows fast and distributed computation. This will allow us to search for interesting swarm intelligent robotic algorithms for creating novel bio-hybrid systems composed by real animals and autonomous rule-driven cellular robots by using stochastic optimization techniques.