Hive Collective Intelligence for Cloud Robotics: A Hybrid Distributed Robotic Controller Design for Learning and Adaptation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 351)


The recent advent of Cloud Computing, inevitably gave rise to Cloud Robotics. Whilst the field is arguably still in its infancy, great promise is shown regarding the problem of limited computational power in Robotics. This is the most evident advantage of Cloud Robotics, but, other much more significant yet subtle advantages can now be identified. Moving away from traditional Robotics, and approaching Cloud Robotics through the prism of distributed systems or Swarm Intelligence offers quite an interesting composure; physical robots deployed across different areas, may delegate tasks to higher intelligence agents residing in the cloud. This design has certain distinct attributes, similar with the organisation of a Hive or bee colony. Such a parallelism is crucial for the foundations set hereinafter, as they express through the hive design, a new scheme of distributed robotic architectures. Delegation of agent intelligence, from the physical robot swarms to the cloud controllers, creates a unique type of Hive Intelligence, where the controllers residing in the cloud, may act as the brain of a ubiquitous group of robots, whilst the robots themselves act as proxies for the Hive Intelligence. The sensors of the hive system providing the input and output are the robots, yet the information processing may take place collectively, individually or on a central hub, thus offering the advantages of a hybrid swarm and cloud controller. The realisation that radical robotic architectures can be created and implemented with current Artificial Intelligence models, raises interesting questions, such as if robots belonging to a hive, can perform tasks and procedures better or faster, and if can they learn through their interactions, and hence become more adaptive and intelligent.


Robotics Hive Intelligence RAPP Cloud robotics Deep Boltzmann Networks Neural Networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Halsband, U., Lange, R.K.: Motor learning in man: a review of functional and clinical studies. Journal of Physiology 4(6), 414–424 (2006)Google Scholar
  2. 2.
    Haycock, D.E.: Being and Perceiving. Manupod Press (July 31, 2011) ISBN-10: 0956962106Google Scholar
  3. 3.
    Waibel, M., et al.: RoboEarth – A World Wide Web for Robots. IEEE Robotics and Automation Magazine 18(2), 69–82 (2011)CrossRefGoogle Scholar
  4. 4.
    Kamei, K., Nishio, S., Hagita, N., Sato, M.: Cloud Networked Robotics. IEEE Network 26(3), 28–34 (2012)CrossRefGoogle Scholar
  5. 5.
    Quintas, J.M., Menezes, P.J., Dias, J.M.: Cloud Robotics: Toward Context Aware Robotic Networks. In: Proceedings of IASTED, The 16th IASTED International Conference on Robotics (Robo 2011), Pittsburgh, USA, November 7-9 (2011)Google Scholar
  6. 6.
    Chiel, H.J., Beer, R.D.: The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences 12(20), 553–557 (1997)CrossRefGoogle Scholar
  7. 7.
    Mataric, M.J.: Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior. Trends in Cognitive Sciences 3(2), 82–87 (1998)CrossRefGoogle Scholar
  8. 8.
    Warwick, K.: Implications and consequences of robots with biological brains. Ethics and Information Technology 12(3), 223–234 (2010)CrossRefGoogle Scholar
  9. 9.
    Cox, B.R., Krichmar, J.L.: Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots. IEEE Robotics & Automation Magazine 16(3), 72–80 (2009)CrossRefGoogle Scholar
  10. 10.
    Rucci, M., Bullock, D., Santini, F.: Integrating robotics and neuroscience: brains for robots, bodies for brains. Advanced Robotics 21(10), 1115–1129 (2007)CrossRefGoogle Scholar
  11. 11.
    Beni, G., Wang, J.: Swarm Intelligence in Cellular Robotic Systems. In: Advanced Workshop on Robots and Biological Systems, vol. 102, pp. 703–712 (1993)Google Scholar
  12. 12.
    Clerc, M.: Particle Swarm Optimisation. France Telecom, France (2006) ISBN: 9781905209040Google Scholar
  13. 13.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press (2004) ISBN 0-262-04219-3Google Scholar
  14. 14.
    Pham, D.T., Castellani, M.: The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Journal of Mechanical Engineering Science, Part C 223(12), 2919–2938 (2009)CrossRefGoogle Scholar
  15. 15.
    McLurkin, J., Yamins, D.: Dynamic Task Assignment in Robot Swarms. In: Proceedings of Robotics: Science and Systems, pp. 129–136 (2005)Google Scholar
  16. 16.
    Winfield, A.F.T., Nembrini, J.: Safety in numbers: fault-tolerance in robot swarms. International Journal of Modelling, Identification and Control 1(1), 30–37 (2006)CrossRefGoogle Scholar
  17. 17.
    Hinton, G.E.: A Practical Guide to Training Restricted Boltzmann Machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Rolls, T.E., Treves, A.: The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain. Journal Network 1(4), 407–421 (1990)CrossRefGoogle Scholar
  19. 19.
    Wixted, T.J., et al.: Sparse and distributed coding of episodic memory in neurons of the human hippocampus. Proceedings of the National Academy of Sciences, USA 111(26), 9621–9626 (2014)CrossRefGoogle Scholar
  20. 20.
    Le Roux, N., Yoshua, B.: Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. Neural Computation 20(6), 1631–1649 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Ciresan, D., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, High Performance Convolutional Neural Networks for Image Classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237–1242 (2011)Google Scholar
  22. 22.
    Sporns, O., Zwi, J.D.: The small world of the cerebral cortex. Neuroinformatics 2(2), 145–162 (2004)CrossRefGoogle Scholar
  23. 23.
    Hinton, G.: Learning multiple layers of representation. Trends in Cognitive Sciences 11, 428–434 (2007)CrossRefGoogle Scholar
  24. 24.
    Hinton, G.E.: A Practical Guide to Training Restricted Boltzmann Machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  25. 25.
    Ekanadham, C.: Sparse deep belief net model for visual area V2. In: Advances in Neural Information Processing Systems, vol. 20. Curran Associates (2008)Google Scholar
  26. 26.
    Cho, K., Ilin, A., Raiko, T.: Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 10–17. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998) ISBN-10: 9780262193986Google Scholar
  28. 28.
    Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics 37(6), 1554–1563 (1966)CrossRefzbMATHMathSciNetGoogle Scholar
  29. 29.
    Younger, B.A., Fearing, D.D.: Parsing items into separate categories: Developmental change in infant categorization. Child Development 70(2), 291–303 (1999)CrossRefGoogle Scholar
  30. 30.
    Raina, R., Madhavan, A., Ng, A.Y.: Large-scale Deep Unsupervised Learning Using Graphics Processors. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, pp. 873–880 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Ortelio LtdCoventryUK
  2. 2.Centre of Research & Technology, HellasThessalonikiGreece
  3. 3.Department of Electrical and Computer EngineeringAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations