Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques

Nunzia Palmieri, Floriano de Rango, Xin She Yang, Salvatore Marano


In this paper, two metaheuristics are presented for exploration and mine disarming tasks performed by a swarm of robots. The objective is to explore autonomously an unknown area in order to discover the mines, disseminated in the area, and disarm them in cooperative manner since a mine needs multiple robots to disarm. The problem is bi-objective: distributing in different regions the robots in order to explore the area in a minimum amount of time and recruiting the robots in the same location to disarm the mines. While autonomous exploration has been investigated in the past, we specifically focus on the issue of how the swarm can inform its members about the detected mines, and guide robots to the locations. We propose two bio-inspired strategies to coordinate the swarm: the first is based on the Ant Colony Optimization (ATS-RR) and the other is based on the Firefly Algorithm (FTS-RR). Our experiments were conducted by simulations evaluating the performance in terms of exploring and disarming time and the number of accesses in the operative grid area applying both strategies in comparison with the Particle Swarm Optimization (PSO). The results show that FTS-RR strategy performs better especially when the complexity of the tasks increases.


  1. Balch, T. (2005). Communication, diversity and learning: cornerstones of swarm behaviour. In: Swarm robotics, lecture notes in computer science, vol.3342. Springer; p. 21e30.
  2. Bellingham, J. D. and Godin, M. (2007). Robotics in Remote and Hostile Environments. Science, vol. 318, pp. 1098-1102.
  3. De Rango, F. and Palmieri, N. (2012). A swarm-based robot team coordination protocol for mine detection and unknown space discovery. In 8th Int. Conf. on Wireless Communications and Mobile Computing (IWCMC), p. 703-709.
  4. De Rango, F., Palmieri N.,Yang X-S, and Marano. (2015). 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 (SPECTS).
  5. Dorigo, M., Birattari, M. and Stutzle, T. (2006). Ant colony optimization. IEEE Comp. Intell. Mag., Vol. 1, No. 4, 28-39.
  6. Ducatelle, F., Di Caro, G.A., Pinciroli, C. and Gambardella L.M,. (2011). Selforganized cooperation between robotic swarms. Swarm Intelligence, 5(2):73-96.
  7. Fujisawa, R., Dobata, S., Kubota, d., Imamura, H., and Matsuno, F. (2008). Dependency by concentration of pheromone trail for multiple robots. In Proceedings of ANTS 2008, 6th International Workshop on Ant Algorithms and Swarm Intelligence, volume 4217 of LNCS, pages 283-290. Springer.
  8. Garnier, S., Tache, F., Combe, M., Grimal, A., and Theraulaz, G. (2007). Alice in pheromone land: An experimental setup for the study of ant-like robots. In Proc. of the IEEE Swarm Intelligence Symp. (SIS), pages 37-44, Washington, DC, USA.
  9. Jevtic, A., Gutiérrez, A., Andina, D. and Jamshidi, M. (2012). Distributed Bees Algortithm for Task Allocation in Swarm of Robots. IEEE Systems Journal, Vol 6 NO 2.
  10. Masár, M. (2013). A biologically inspired swarm robot coordination algorithm for exploration and surveillance. In Proc. 17th IEEE Int. Conf. on Intelligent Engineering Systems INES, Budapest, pp. 271-275, ISBN 978-1-4799-0830-1.
  11. Mayet, R., Roberz, J., Schmickl, T., & Crailsheim, K. (2010). Antbots: a feasible visual emulation of pheromone trails for swarm robots. In Proceedings of the 7th international conference on swarm intelligence (ANTS) (pp. 84-94).
  12. Meng, Y., Gan, J. A. (2008). A distributed swarm intelligence based algorithm for a cooperative multirobot construction task. Swarm Intelligence Symposium, IEEE Transactions, pp. 1-6.
  13. Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319-324.
  14. Russell, R. (1999). Ant trails - An example for robots to follow. In Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 2698-2703.
  15. Sugawara, K., Kazama, T., and Watanabe, T.(2004). Foraging behavior of interacting robots with virtual pheromone. In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 3074-3079.
  16. Tan, Y. and Zheng, Z-Y. (2013). Research Advance in Swarm Robotics. Defence Technology 9(1):18-39.
  17. Yang, X.S. (2009). Firefly algorithms for multimodal optimization. Lectures Notes in Computer Science, 5792(2009) 169-178.
  18. Yang, X.S. (2010). Firefly algorithm, stochastic test functions and designoptimisation. Int. Journal of BioInspired Computation, 2(2) 78-84
  19. Yang, X.S. (2014). Cuckoo Search and Firefly Algorithm Theory and Applications. Book on Studies in Computational Intelligence, ISBN: 978-3-319-02140- 9.

Paper Citation

in Harvard Style

Palmieri N., de Rango F., She Yang X. and Marano S. (2015). Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 74-82. DOI: 10.5220/0005596200740082

in Bibtex Style

author={Nunzia Palmieri and Floriano de Rango and Xin She Yang and Salvatore Marano},
title={Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques
SN - 978-989-758-157-1
AU - Palmieri N.
AU - de Rango F.
AU - She Yang X.
AU - Marano S.
PY - 2015
SP - 74
EP - 82
DO - 10.5220/0005596200740082