Triangle Formation Based Multiple Targets Search Using a Swarm of Robots

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9713)

Abstract

As a distributed system, swarm robotics is well suited for multiple targets search tasks. In this paper, a new approach based on triangle formation and random search is proposed for high efficiency, demonstrating excellent abilities of exploration and exploitation in experiments. In addition, a new random walk strategy of linear ballistic motion, integrated with triangle estimation, is put forward as a comparison algorithm, the performance of which can serve as a benchmark.

Keywords

Swarm robotics Multiple targets search Triangle formation Random search Exploration and exploitation 

References

  1. 1.
    Tan, Y.: A Survey on Swarm Robotics. In: Li, J. (ed.) Handbook of Research on Design, Control, and Modeling of Swarm Robotics, 1. IGI Global, Hershey (2015)Google Scholar
  2. 2.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Li, J., Tan, Y.: The multi-target search problem with environmental restrictions in swarm robotics. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2685–2690. IEEE, December 2014Google Scholar
  4. 4.
    Gazi, V., Passino, K.M.: Stability analysis of social foraging swarms. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 539–557 (2004)CrossRefGoogle Scholar
  5. 5.
    Krishnanand, K.N., Ghose, D.: A glowworm swarm optimization based multi-robot system for signal source localization. In: Liu, D., Wang, L., Tan, K.C. (eds.) Design and Control of Intelligent Robotic Systems. SCI, vol. 177, pp. 49–68. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Couceiro, M.S., Rocha, R.P., Ferreira, N.M.: A novel multi-robot exploration approach based on particle swarm optimization algorithms. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 327–332. IEEE, November 2011Google Scholar
  7. 7.
    Zheng, Z., Tan, Y.: Group explosion strategy for searching multiple targets using swarm robotic. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 821–828. IEEE, June 2013Google Scholar
  8. 8.
    Zheng, Z., Li, J., Li, J., Tan, Y.: Improved group explosion strategy for searching multiple targets using swarm robotics. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 246–251. IEEE, October 2014Google Scholar
  9. 9.
    Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Zheng, Z., Li, J., Li, J., Tan, Y.: Avoiding decoys in multiple targets searching problems using swarm robotics. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 784–791. IEEE, July 2014Google Scholar
  11. 11.
    Shlesinger, M.F.: Mathematical physics: search research. Nature 443(7109), 281–282 (2006)CrossRefGoogle Scholar
  12. 12.
    Viswanathan, G.M., Buldyrev, S.V., Havlin, S., Da Luz, M.G.E., Raposo, E.P., Stanley, H.E.: Optimizing the success of random searches. Nature 401(6756), 911–914 (1999)CrossRefGoogle Scholar
  13. 13.
    Bartumeus, F., Raposo, E.P., Viswanathan, G.M., da Luz, M.G.: Stochastic optimal foraging theory. In: Lewis, M.A., Maini, P.K., Petrovskii, S.V. (eds.) Dispersal, Individual Movement and Spatial Ecology. LNM, vol. 2071, pp. 3–32. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Balch, T., Arkin, R.C.: Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)CrossRefGoogle Scholar
  15. 15.
    Amory, A., Meyer, B., Osterloh, C., Tosik, T., Maehle, E.: Towards fault-tolerant and energy-efficient swarms of underwater robots. In: 2013 IEEE 27th International Parallel and Distributed Processing Symposium Workshops and Ph.D. Forum (IPDPSW), pp. 1550–1553. IEEE, May 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Key Laboratory of Machine Perception and Intelligence, Ministry of EducationPeking UniversityBeijingChina
  2. 2.Department of Machine Intelligence, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations