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Trophallaxis, Low-Power Vision Sensors and Multi-objective Heuristics for 3D Scene Reconstruction Using Swarm Robotics

  • Maria Carrillo
  • Javier Sánchez-Cubillo
  • Eneko Osaba
  • Miren Nekane Bilbao
  • Javier Del SerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)

Abstract

A profitable strand of literature has lately capitalized on the exploitation of the collaborative capabilities of robotic swarms for efficiently undertaking diverse tasks without any human intervention, ranging from the blind exploration of devastated areas after massive disasters to mechanical repairs of industrial machinery in hostile environments, among others. However, most contributions reported to date deal only with robotic missions driven by a single task-related metric to be optimized by the robotic swarm, even though other objectives such as energy consumption may conflict with the imposed goal. In this paper four multi-objective heuristic solvers, namely NSGA-II, NSGA-III, MOEA/D and SMPSO, are used to command and route a set of robots towards efficiently reconstructing a scene using simple camera sensors and stereo vision in two phases: explore the area and then achieve validated map points. The need for resorting to multi-objective heuristics stems, from the consideration of energy efficiency as a second target of the mission plan. In this regard, by incorporating energy trophallaxis within the swarm, overall autonomy is increased. An environment is arranged in V-REP to shed light on the performance over a realistically emulated physical environment. SMPSO shows better exploration capabilities during the first phase of the mission. However, in the second phase the performance of SMPSO degrades in contrast to NSGA-II and NSGA-III. Moreover, the entire robotic swarm is able to return to the original departure position in all the simulations. The obtained results stimulate further research lines aimed at considering decentralized heuristics for the considered problem.

Keywords

Swarm robotics Scene reconstruction Stereo vision Energy trophallaxis Multi-objective heuristics 

Notes

Acknowledgements

This work was supported by the Basque Government through the EMAITEK program.

References

  1. 1.
    Beni, G.: From swarm intelligence to swarm robotics. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 1–9. Springer, Heidelberg (2005).  https://doi.org/10.1007/978-3-540-30552-1_1CrossRefGoogle Scholar
  2. 2.
    Tan, Y., Zheng, Z.: Research advance in swarm robotics. Defence Technol. 9(1), 18–39 (2013)CrossRefGoogle Scholar
  3. 3.
    Ben-Ari, M., Mondada, F.: Swarm robotics. In: Elements of Robotics, pp. 251–265. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-62533-1_15
  4. 4.
    Wong, C., Yang, E., Yan, X.T., Gu, D.: Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges. Syst. Sci. Control Eng. 6(1), 213–219 (2018)CrossRefGoogle Scholar
  5. 5.
    Wong, C., Yang, E., Yan, X.T., Gu, D.: An overview of robotics and autonomous systems for harsh environments. In: International Conference on Automation and Computing, pp. 1–6 (2017)Google Scholar
  6. 6.
    Barca, J.C., Sekercioglu, Y.A.: Swarm robotics reviewed. Robotica 31(3), 345–359 (2013)CrossRefGoogle Scholar
  7. 7.
    Korst, P., Velthuis, H.: The nature of trophallaxis in honeybees. Insectes Soc. 29(2), 209–221 (1982)CrossRefGoogle Scholar
  8. 8.
    Hamilton, C., Lejeune, B.T., Rosengaus, R.B.: Trophallaxis and prophylaxis: social immunity in the carpenter ant camponotus pennsylvanicus. Biol. Lett. 7(1), 89–92 (2011)CrossRefGoogle Scholar
  9. 9.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  10. 10.
    Nebro, A.J., Durillo, J.J., García-Nieto, J., Coello Coello, C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 66–73 (2009)Google Scholar
  11. 11.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 8(11), 712–731 (2008)Google Scholar
  12. 12.
    Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)CrossRefGoogle Scholar
  13. 13.
    Haek, M., Ismail, A.R., Basalib, A.O.A., Makarim, N.: Exploring energy charging problem in swarm robotic systems using foraging simulation. Jurnal Teknologi 76(1), 239–244 (2015)Google Scholar
  14. 14.
    Schmickl, T., Crailsheim, K.: Trophallaxis among swarm-robots: a biologically inspired strategy for swarm robotics. In: IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 377–382 (2006)Google Scholar
  15. 15.
    Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton. Robots 25(1–2), 171–188 (2008)CrossRefGoogle Scholar
  16. 16.
    Melhuish, C., Kubo, M.: Collective energy distribution: maintaining the energy balance in distributed autonomous robots using trophallaxis. Distrib. Auton. Robot. Syst. 6, 275–284 (2007)CrossRefGoogle Scholar
  17. 17.
    Schiøler, H., Ngo, T.D.: Trophallaxis in robotic swarms-beyond energy autonomy. In: International Conference on Control, Automation, Robotics and Vision, pp. 1526–1533 (2008)Google Scholar
  18. 18.
    Carrillo, M., et al.: A bio-inspired approach for collaborative exploration with mobile battery recharging in swarm robotics. In: Korošec, P., Melab, N., Talbi, E.-G. (eds.) BIOMA 2018. LNCS, vol. 10835, pp. 75–87. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91641-5_7CrossRefGoogle Scholar
  19. 19.
    Mostaghim, S., Steup, C., Witt, F.: Energy aware particle swarm optimization as search mechanism for aerial micro-robots. In: IEEE Symposium Series on Computational Intelligence, pp. 1–7 (2016)Google Scholar
  20. 20.
    Ismail, A.R., Desia, R., Zuhri, M.F.R.: The initial investigation of the design and energy sharing algorithm using two-ways communication mechanism for swarm robotic systems. In: Phon-Amnuaisuk, S., Au, T.W. (eds.) Computational Intelligence in Information Systems. AISC, vol. 331, pp. 61–71. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-13153-5_7CrossRefGoogle Scholar
  21. 21.
    Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263 (2008)CrossRefGoogle Scholar
  22. 22.
    Hong, S., Li, M., Liao, M., van Beek, P.: Real-time mobile robot navigation based on stereo vision and low-cost GPS. Electron. Imaging 2017, 10–15 (2017)CrossRefGoogle Scholar
  23. 23.
    Sugihara, K.: Three principles in stereo vision. Adv. Robot. 1(4), 391–400 (1986)CrossRefGoogle Scholar
  24. 24.
    Pollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vis. 32(1), 7–25 (1999)CrossRefGoogle Scholar
  25. 25.
    Mattoccia, S., De-Maeztu, L.: A fast segmentation-driven algorithm for accurate stereo correspondence. In: International Conference on 3D Imaging, pp. 1–6 (2011)Google Scholar
  26. 26.
    Chrysostomou, D., Gasteratos, A., Nalpantidis, L., Sirakoulis, G.C.: Multi-view 3D scene reconstruction using ant colony optimization techniques. Meas. Sci. Technol. 23(11), 114002 (2012)CrossRefGoogle Scholar
  27. 27.
    Rohmer, E., Singh, S.P., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: International Conference on Intelligent Robots and Systems (IROS), pp. 1321–1326. IEEE (2013)Google Scholar
  28. 28.
    De Meyer, K., Slawomir, N.J., Mark, B.: Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimisation. In: Swarm Intelligence Dynamic Optimisation, pp. 185–207. Springer, Heidelberg (2006)Google Scholar
  29. 29.
    Zhu, D., Tian, C., Sun, B., Luo, C.: Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm. J. Intell. Robot. Syst. 1–13 (2018). https://link.springer.com/article/10.1007/s10846-018-0787-7
  30. 30.
    Horvátha, E., Pozna, C., Precup, R.E.: Robot coverage path planning based on iterative structured orientation. Acta Polytechnica Hungarica 15(2), 231–249 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maria Carrillo
    • 1
  • Javier Sánchez-Cubillo
    • 2
  • Eneko Osaba
    • 2
  • Miren Nekane Bilbao
    • 1
  • Javier Del Ser
    • 1
    • 2
    Email author
  1. 1.University of the Basque Country (UPV/EHU)BilbaoSpain
  2. 2.TECNALIA Research & InnovationDerioSpain

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