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Bio-inspired Strategies for the Coordination of a Swarm of Robots in an Unknown Area

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Computational Intelligence (IJCCI 2015)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 669))

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Abstract

This paper addresses the problem of searching mines in an unknown area and disarming them in a cooperative manner. We describe two bio-inspired mechanisms that allow the robots to initiate the coordination with other robots when a mine is discovered. We model this problem as a multi-objective exploration and disarming problem. Specifically we propose a modified version of the Ant Colony Optimization (ATS-RR) and the Firefly Algorithm (FTS-RR). The proposed approaches have been implemented and evaluated in several simulated environments varying the parameter of the problems in term of team sizes, the number of mines disseminated in the area, the dimension of area. Our approaches have been implemented in simulation environments and have been compared with Particle Swarm Optimization (PSO). The results demonstrate the efficiency of the FTS-RR over others.

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References

  1. Bellingham, J.D., Godin, M.: Robotics in remote and hostile environments. Science 318, 1098–1102 (2007)

    Article  Google Scholar 

  2. Ducatelle, F., Di Caro, G.A., Förster, A., Bonani, M., Dorigo, M., Magnenat, S., Mondada, F., O’Grady, R., Pinciroli, C., Rétornaz, P., et al.: Cooperative navigation in robotic swarms, Swarm Intell., 1–33 (2014)

    Google Scholar 

  3. Tan, Y., Zheng, Z.-Y.: Research advance in swarm robotics. Defence Technol. 9(1), 18–39 (2013)

    Article  Google Scholar 

  4. De Rango, F., Palmieri, N.: A swarm-based robot team coordination protocol for mine detection and unknown space discovery. In: 8th International Conference on Wireless Communications and Mobile Computing (IWCMC), pp. 703–709 (2012)

    Google Scholar 

  5. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  6. De Rango, F., Palmieri N., Yang, X.S., Marano, S: Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions. In: International Symposium on Performance Evaluation of Computer and Telecommunication System, Chicago (2015)

    Google Scholar 

  7. Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  8. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)

    Article  Google Scholar 

  9. Yang, X.S.: Cuckoo Search and Firefly Algorithm Theory and Applications. Book on Studies in Computational Intelligence, ISBN: 978-3-319-02140-9 (2014)

    Google Scholar 

  10. Russell, R.: Ant trails - an example for robots to follow. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 2698–2703 (1999)

    Google Scholar 

  11. Sugawara, K., Kazama, T., Watanabe, T.: Foraging behavior of interacting robots with virtual pheromone. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3074–3079 (2004)

    Google Scholar 

  12. Garnier, S., Tache, F., Combe, M., Grimal, A., Theraulaz, G.: Alice in pheromone land: an experimental setup for the study of ant-like robots. In: Proceedings of the IEEE Swarm Intelligence Symposium (SIS), pp. 37–44, Washington, DC, USA (2007)

    Google Scholar 

  13. Ducatelle, F., Di Caro, G.A., Pinciroli, C., Gambardella, L.M.: Self organized cooperation between robotic swarms. Swarm Intell. 5(2), 73–96 (2011)

    Article  Google Scholar 

  14. Masár, M.: A biologically inspired swarm robot coordination algorithm for exploration and surveillance. In: Proceedings of 17th IEEE International Conference on Intelligent Engineering Systems INES, Budapest, pp. 271–275, ISBN 978-1-4799-0830-1 (2013)

    Google Scholar 

  15. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., Matsuno, F.: Dependency by concentration of pheromone trail for multiple robots. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 283–290. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87527-7_28

    Chapter  Google Scholar 

  16. Mayet, R., Roberz, J., Schmickl, T., Crailsheim, K.: Antbots: a feasible visual emulation of pheromone trails for swarm robots. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 84–94. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15461-4_8

    Chapter  Google Scholar 

  17. Payton, D., Daily, M., Estowski, R., Howard, M., Lee, C.: Pheromone robotics. Auton. Robots 11(3), 319–324 (2001)

    Article  MATH  Google Scholar 

  18. Labella, T.H., Dorigo, M., Deneubourg, J.L.: Division of labour in a group inspired by ants’ foraging behaviour. ACM Trans. Auton. Adapt. Syst. 1(1), 4–25 (2006)

    Article  Google Scholar 

  19. Meng, Y., Gan, J.: A distributed swarm intelligence based algorithm for a cooperative multi-robot construction task. In: IEEE Swarm Intelligence Symposium (2008)

    Google Scholar 

  20. Pugh, J., Martinoli, A.: Multi-robot learning with particle swarm optimization. In: Proceedings of Fifth International Joint Conference on Autonomous Robots and Multirobot Systems, pp. 441–448, Japan (2006)

    Google Scholar 

  21. Hereford, J.M., Siebold, M., Nichols, S.: Using the particle swarm optimization algorithm for robotic search applications. In: IEEE Swarm Intelligence Symposium (2007)

    Google Scholar 

  22. Jevtic, A., Gutiérrez, A., Andina, D., Jamshidi, M.: Distributed Bees algorithm for task allocation in swarm of robots. IEEE Syst. J. 6(2), 296–304 (2012)

    Article  Google Scholar 

  23. Balch, T.: Communication, diversity and learning: cornerstones of swarm behavior. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 21–30. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30552-1_3

    Chapter  Google Scholar 

  24. Mohan, Y., Ponnambalam, S.: An extensive review of research in swarm robotics. In: Nature and Biologically Inspired Computing, NaBIC, Word Congress (2009)

    Google Scholar 

  25. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering Modeling, Electronics and System Science, University of Calabria, Rende (CS), Italy

    Nunzia Palmieri, Floriano de Rango & Salvatore Marano

  2. School of Science and Technology, Middlesex University, The Burroughs, London, UK

    Nunzia Palmieri & Xin She Yang

Authors
  1. Nunzia Palmieri
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  2. Floriano de Rango
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  3. Xin She Yang
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  4. Salvatore Marano
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Corresponding author

Correspondence to Nunzia Palmieri .

Editor information

Editors and Affiliations

  1. Computer Architecture and Computer Technology, Universidad de Granada, Granada, Spain

    Juan Julián Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    António Dourado Correia

  5. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  6. Campus de Gambelas, Universidade do Algarve, Faro, Portugal

    António Ruano

  7. Escola Superior de Tecnologia de Setúbal, Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Palmieri, N., de Rango, F., Yang, X.S., Marano, S. (2017). Bio-inspired Strategies for the Coordination of a Swarm of Robots in an Unknown Area. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-48506-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48504-1

  • Online ISBN: 978-3-319-48506-5

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