Study of Communication Issues in Dynamically Scalable Cloud-Based Vision Systems for Mobile Robots

  • Javier Salmerón-GarcíaEmail author
  • Pablo Iñigo-Blasco
  • Fernando Díaz-del-Río
  • Daniel Cagigas-Muñiz
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 36)


Thanks to the advent of technologies like Cloud Computing, the idea of computation offloading of robotic tasks is more than feasible. Therefore, it is possible to use legacy embedded systems for computationally heavy tasks like navigation or artificial vision, hence extending its lifespan. In this chapter we apply Cloud Computing for building a Cloud-Based 3D Point Cloud extractor for stereo images. The objective is to have a dynamically scalable solution (one of Cloud Computing’s most important features) and applicable to near real-time scenarios. This last feature brings several challenges that must be addressed: meeting of deadlines, stability, limitation of communication technologies. All those elements will be thoroughly analyzed in this chapter, providing experimental results that prove the efficacy of the solution. At the end of the chapter, a successful use case of the platform is explained: navigation assistance.


Cloud computing Computation offloading Robotics Dynamic scalability 



The work shown in this chapter has been supported by the Spanish grant (with support from the European Regional Development Fund) BIOSENSE (TEC2012-37868-C04-02/01) and by Andalusian Regional Excellence Research Project grant (with support from the European Regional Development Fund) MINERVA (P12-TIC-1300). We wish to thank also Prof. D. Cascado for his interesting comments.


  1. 1.
    Agostinho, L., Olivi, L., Feliciano, G., Paolieri, F., Rodrigues, D., Cardozo, E., Guimaraes, E.: A cloud computing environment for supporting networked robotics applications. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 1110–1116 (2011). doi: 10.1109/DASC.2011.181
  2. 2.
    Arumugam, R., Enti, V., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F.F., Kumar, A., Meng, K.D., Kit, G.W.: DAvinCi: a cloud computing framework for service robots. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3084–3089 (2010). doi: 10.1109/ROBOT.2010.5509469
  3. 3.
    Bistry, H., Zhang, J.: A cloud computing approach to complex robot vision tasks using smart camera systems. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3195–3200 (2010). doi: 10.1109/IROS.2010.5653660
  4. 4.
    Buyya, R., Vecchiola, C., Selvi, S.T.: Mastering Cloud Computing Foundations and Applications Programming. Morgan Kaufmann/Elsevier, Amsterdam (2013)Google Scholar
  5. 5.
    Charfi, E., Chaari, L., Kamoun, L.: PHY/MAC enhancements and QoS mechanisms for very high throughput WLANs: a survey. IEEE Commun. Surv. Tutor. 15(4), 1714–1735 (2013). doi: 10.1109/SURV.2013.013013.00084 CrossRefGoogle Scholar
  6. 6.
    Cloud-based robot navigation assistant using stereo image processing. (2014). Accessed 12 Nov 2014
  7. 7.
    Furht, B., Escalante, A. (eds.): Handbook of Cloud Computing. Springer, New York (2010)zbMATHGoogle Scholar
  8. 8.
    Gouveia, B.D., Portugal, D., Silva, D.C., Marques, L.: Computation sharing in distributed robotic systems: a case study on SLAM. IEEE Trans. Autom. Sci. Eng. (T-ASE) 12(2), 410–422 (2015)CrossRefGoogle Scholar
  9. 9.
    Guizzo, E.: Robots with their heads in the clouds. IEEE Spectr. 48(3), 16–18 (2011). doi: 10.1109/MSPEC.2011.5719709 CrossRefGoogle Scholar
  10. 10.
    Handa, A., Newcombe, R.A., Angeli, A., Davison, A.J.: Real-time camera tracking: when is high frame-rate best? In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision ECCV 2012. Lecture Notes in Computer Science, vol. 7578, pp. 222–235. Springer, Berlin (2012)Google Scholar
  11. 11.
    Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach. Morgan Kaufmann, Waltham (2012)Google Scholar
  12. 12.
    Iñigo-Blasco, P., Diaz-del Rio, F., Romero-Ternero, M.C., Cagigas-Muñiz, D., Vicente-Diaz, S.: Robotics software frameworks for multi-agent robotic systems development. Robot. Auton. Syst. 60(6), 803–821 (2012). doi: 10.1016/j.robot.2012.02.004.
  13. 13.
    Iñigo-Blasco, P., Diaz-del Rio, F., Vicente-Diaz, S., Cagigas-Muñiz, D.: The shared control dynamic window approach for non-holonomic semi-autonomous robots. In: Proceedings of 41st International Symposium on Robotics. ISR/Robotik 2014. Munich (2014)Google Scholar
  14. 14.
    John, B.P.: Effectiveness of SPEC CPU2006 and Multimedia Applications on Intel’s Single. Dual and Quad Core Processors. ProQuest (2009)Google Scholar
  15. 15.
    Kytö, M., Nuutinen, M., Pirkko, O.: Method for measuring stereo camera depth accuracy based on stereoscopic vision. In: Proceedings of SPIE/IS&T Electronic Imaging 2011 (2011)Google Scholar
  16. 16.
    Nimmagadda, Y., Kumar, K., Lu, Y.H., Lee, C.S.G.: Real-time moving object recognition and tracking using computation offloading. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2449–2455 (2010). doi: 10.1109/IROBlS.2010.5650303
  17. 17.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: ROS: an open-source robot operating system (2009)Google Scholar
  18. 18.
    Riazuelo, L., Civera, J., Montiel, J.M.M.: C2tam: a cloud framework for cooperative tracking and mapping. Robot. Auton. Syst. 62(4), 401–413 (2014). doi: 10.1016/j.robot.2013.11.007 CrossRefGoogle Scholar
  19. 19.
    Salmeron-Garcia, J., Iñigo Blasco, P., Diaz-del Rio, F., Cagigas-Muniz, D.: A trade-off analysis of a cloud-based robot navigation assistant using stereo image processing. IEEE Trans. Autom. Sci. Eng. (T-ASE) 12(2), 444–454 (2015)CrossRefGoogle Scholar
  20. 20.
    Srinivasan, S.: Cloud Computing Basics. Springer Briefs in Electrical and Computer Engineering. Springer, New York (2014)CrossRefGoogle Scholar
  21. 21.
    Stallings, W.: Data and Computer Communications. Always Learning, 10th edn. Pearson, Boston (2014)Google Scholar
  22. 22.
    Szeliski, R.: Computer Vision Algorithms and Applications. Texts in Computer Science. Springer, London (2011)zbMATHGoogle Scholar
  23. 23.
    Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Galvez-Lopez, D., Haussermann, K., Janssen, R., Montiel, J.M.M., Perzylo, A., Schiessle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: RoboEarth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011). doi: 10.1109/MRA.2011.941632 CrossRefGoogle Scholar
  24. 24.
    Wu, H., Lou, L., Chen, C.C., Hirche, S., Kuhnlenz, K.: Cloud-based networked visual servo control. IEEE Trans. Ind. Electron. 60(2), 554–566 (2013). doi: 10.1109/TIE.2012.2186775 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier Salmerón-García
    • 1
    Email author
  • Pablo Iñigo-Blasco
    • 1
  • Fernando Díaz-del-Río
    • 1
  • Daniel Cagigas-Muñiz
    • 1
  1. 1.Escuela Técnica Superior de Ingeniería InformáticaUniversity of SevilleSevillaSpain

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