Swarm Intelligence

, Volume 8, Issue 1, pp 61–87 | Cite as

Cache consensus: rapid object sorting by a robotic swarm

Article
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Abstract

We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots.

Keywords

Swarm robotics Patch sorting Clustering Localisation 
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Notes

Acknowledgments

Thanks to Paul Gillard for helpful discussions and support in diagnosing myriad hardware problems. WB gratefully acknowledges funding from NSERC under Discovery Grant RGPIN 283304-2012.

Supplementary material

11721_2014_91_MOESM1_ESM.pdf (629 kb)
Supplementary material 1 (pdf 630 KB)

References

  1. Beckers, R., Holland, O., & Deneubourg, J. L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In R. Brooks & P. Maes (Eds.), Artificial life IV (pp. 181–189). Cambridge, MA: MIT Press.Google Scholar
  2. Beekman, M., Sword, G. A., & Simpson, S. J. (2008). Biological foundations of swarm intelligence. In C. Blum & D. Merkle (Eds.), Swarm intelligence, natural computing series (pp. 3–41). Berlin: Springer.Google Scholar
  3. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. New York, NY: Oxford University Press.MATHGoogle Scholar
  4. Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), 14–23.CrossRefGoogle Scholar
  5. Cartwright, B., & Collett, T. (1983). Landmark learning in bees. Journal of Comparative Physiology A, 151, 521–543.CrossRefGoogle Scholar
  6. Collett, T., & Collett, M. (2002). Memory use in insect visual navigation. Nature Reviews Neuroscience, 3, 542–552.CrossRefGoogle Scholar
  7. Deneubourg, J. L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., & Chrétien, L. (1990). The dynamics of collective sorting robot-like ants and ant-like robots. In First International Conference on the Simulation of Adaptive Behaviour (pp. 356–363). Cambridge, MA: MIT Press.Google Scholar
  8. Dudek, G., & Jenkin, M. (2010). Computational principles of mobile robotics (2nd ed.). New York, NY: Cambridge University Press.CrossRefMATHGoogle Scholar
  9. Gonzalez, R., & Woods, R. (2002). Digital image processing (2nd ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  10. Gordon, D. (1996). The organization of work in social insect colonies. Nature, 380, 121–124.CrossRefGoogle Scholar
  11. Gutiérrez, A., Campo, A., Monasterio-Huelin, F., Magdalena, L., & Dorigo, M. (2010). Collective decision-making based on social odometry. Neural Computing and Applications, 19(6), 807–823. doi: 10.1007/s00521-010-0380-x.CrossRefGoogle Scholar
  12. House, B., Capson, D., & Schuurman, D. (2011). Towards real-time sorting of recyclable goods using support vector machines. In Sustainable Systems and Technology (ISSST), 2011 IEEE International Symposium on IEEE Xplore (pp. 1–6). doi: 10.1109/ISSST.2011.5936845.
  13. Katz, D., Orthey, A., & Brock, O. (2010). Interactive perception of articulated objects. In 12th International Symposium of Experimental Robotics (pp. 1–15). Berlin: Springer.Google Scholar
  14. Kazadi, S., Abdul-Khaliq, A., & Goodman, R. (2002). On the convergence of puck clustering systems. Robotics and Autonomous Systems, 38(2), 93–117.CrossRefMATHGoogle Scholar
  15. Maris, M., & Boeckhorst, R. (1996). Exploiting physical constraints: heap formation through behavioral error in a group of robots. In IEEE/RSJ International Conference on Robots and Systems (IROS), IEEE Xplore, Vol. 3 (pp 1655–1660).Google Scholar
  16. Martinoli, A., Ijspeert, A., & Gambardella, L. (1999). A probabilistic model for understanding and comparing collective aggregation mechanisms. In D. Floreano, J.-D. Nicoud, & F. Mondada (Eds.), Advances in artificial life, lecture notes in computer science (Vol. 1674, pp. 575–584). Berlin: Springer.Google Scholar
  17. Melhuish, C., Holland, O., & Hoddell, S. (1998). Collective sorting and segregation in robots with minimal sensing. In 5th International Conference on the Simulation of Adaptive Behaviour. Cambridge, MA: MIT Press.Google Scholar
  18. Melhuish, C., Sendova-Franks, A. B., Scholes, S., Horsfield, I., & Welsby, F. (2006). Ant-inspired sorting by robots: The importance of initial clustering. Journal of the Royal Society: Interface, 3(7), 235–242.Google Scholar
  19. Melhuish, C., Wilson, M., & Sendova-Franks, A. B. (2001). Patch sorting: Multi-object clustering using minimalist robots. In Advances in Artificial Life—Proceedings of the 6th European Conference on Artificial Life (ECAL). Springer.Google Scholar
  20. Möller, R., Krzykawski, M., & Gerstmayr, L. (2010). Three 2D-warping schemes for visual robot navigation. Autonomous Robots, 29(3), 253–291.CrossRefGoogle Scholar
  21. Olson, E. (2011). AprilTag: A robust and flexible visual fiducial system. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE Xplore (pp. 3400–3407).Google Scholar
  22. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). ROS: An open-source robot operating system. In ICRA Workshop on Open Source Software.Google Scholar
  23. Scaramuzza, D., Martinelli, A., & Siegwart, R. (2006). A toolbox for easily calibrating omnidirectional cameras. In IEEE/RSJ International Conference on Robots and Systems (IROS), IEEE Xplore (pp. 5695–5701).Google Scholar
  24. Seeley, T. D. (2010). Honeybee democracy. Princeton, NJ: Princeton University Press.Google Scholar
  25. Sendova-Franks, A. B., Scholes, S. R., Franks, N. R., & Melhuish, C. (2004). Brood sorting by ants: Two phases and differential diffusion. Animal Behavior, 68, 1095–1106.CrossRefGoogle Scholar
  26. Sharkey, A. J. (2007). Swarm robotics and minimalism. Connection Science, 19(3), 245–260.CrossRefGoogle Scholar
  27. Siegwart, R., Nourbakhsh, I., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  28. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge, MA: MIT Press.MATHGoogle Scholar
  29. Trucco, E., & Verri, A. (1998). Introductory techniques for 3D computer vision. Upper Saddle River: Prentice Hall.Google Scholar
  30. Ulrich, I., & Borenstein, J. (1998). VFH+: Reliable obstacle avoidance for fast mobile robots. In IEEE International Conference on Robotics and Automation (ICRA), IEEE Xplore, Vol. 2 (pp. 1572–1577).Google Scholar
  31. Vardy, A. (2012). Accelerated patch sorting by a robotic swarm. In Canadian Conference on Computer and Robot Vision, IEEE Xplore (pp. 314–321).Google Scholar
  32. Vardy, A., & Möller, R. (2005). Biologically plausible visual homing methods based on optical flow techniques. Connection Science, 17(1/2), 47–90.CrossRefGoogle Scholar
  33. Verret, S., Zhang, H., & Meng, M. Q. H. (2004). Collective sorting with local communication. In IEEE/RSJ International Conference on Robots and Systems (IROS), IEEE Xplore, Vol. 3 (pp. 2687–2692).Google Scholar
  34. Wang, T., & Zhang, H. (2003). Multi-robot collective sorting with local sensing. In IEEE Intelligent Automation Conference (IAC).Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Andrew Vardy
    • 1
  • Gregory Vorobyev
    • 2
  • Wolfgang Banzhaf
    • 2
  1. 1.Department of Computer Science, Faculty of Engineering & Applied ScienceMemorial University of NewfoundlandSt. John’sCanada
  2. 2.Department of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

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