Cloud-Based Services for Cooperative Robot Learning of 3D Object Detection and Recognition

  • Parkpoom ChaisiriprasertEmail author
  • Karn Yongsiriwit
  • Apiporn Simapornchai
  • Matthew Dailey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 807)


Some of the problems preventing the widespread adoption of advanced service robots are (1) cost, (2) power consumption, (3) perceptual capabilities, (4) knowledge management, (5) reasoning capabilities, and (6) compute power. Improvement along these dimensions will make adoption of service robots with advanced capabilities much more widespread. We propose the use of real time cloud computation as one means to enable these improvements. This paper presents a case study on the use of a cloud computing platform to support robotics applications, allowing robots to offload heavy compute tasks such as machine vision to cloud infrastructure. We specifically aim to give a widely distributed group of robots the ability to learn 3D objects cooperatively for detection and recognition. Participating robots can share their knowledge with others via our cloud-based services. Experiments with a proof of concept prototype demonstrate the feasibility of the use of cloud platforms to deliver improved perceptual, knowledge management, and reasoning capabilities while keeping the cost and power consumption of the robot low.


Cloud-based services Machine vision 3D object detection and recognition Cooperative robot learning 


  1. 1.
    Cao, Y.U., Fukunaga, A.S., Kahng, A.B., Meng, F.: Cooperative mobile robotics: antecedents and directions. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 1995, Human Robot Interaction and Cooperative Robots, Proceedings 1995, vol. 1, pp. 226–234, August 1995Google Scholar
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: a berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley, February 2009Google Scholar
  3. 3.
    Jordan, S., Haidegger, T., Kovacs, L., Felde, I., Rudas, I.: The rising prospects of cloud robotic applications. In: 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 327–332, July 2013Google Scholar
  4. 4.
    Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation. IEEE Trans. Autom. Sci. Eng. 12(2), 398–409 (2015)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Du, Z., García-Acosta, M.: Robot as a service in cloud computing. In: Fifth IEEE International Symposium on Service Oriented System Engineering (SOSE) 2010, pp. 151–158, June 2010Google Scholar
  6. 6.
    Du, Z., Yang, W., Chen, Y., Sun, X., Wang, X., Xu, C.: Design of a robot cloud center. In: 10th International Symposium on Autonomous Decentralized Systems (ISADS) 2011, pp. 269–275, March 2011Google Scholar
  7. 7.
    Agostinho, L., Olivi, L., Feliciano, G., Paolieri, F., Rodrigues, D., Cardozo, E., Guimaraes, E.: A cloud computing environment for supporting networked robotics applications. In: IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC) 2011, pp. 1110–1116 (2011)Google Scholar
  8. 8.
    Arumugam, R., Enti, V.R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F.F., Kumar, A.S., Meng, K.D., Kit, G.W.: DAvinCi: a cloud computing framework for service robots. In: IEEE International Conference on Robotics and Automation (ICRA) 2010, pp. 3084–3089, May 2010Google Scholar
  9. 9.
    Chen, Y.-Y., Wang, J.-F., Lin, P.-C., Shih, P.-Y., Tsai, H.-C., Kwan, D.-Y.: Human-robot interaction based on cloud computing infrastructure for senior companion. In: TENCON 2011-2011 IEEE Region 10 Conference, pp. 1431–1434, November 2011Google Scholar
  10. 10.
    Tenorth, M., Kamei, K., Satake, S., Miyashita, T., Hagita, N.: Building knowledge-enabled cloud robotics applications using the ubiquitous network robot platform. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5716–5721, November 2013Google Scholar
  11. 11.
    Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., Goldberg, K.: Cloudbased robot grasping with the Google object recognition engine. In: IEEE International Conference on Robotics and Automation (ICRA) 2013, pp. 4263–4270, May 2013Google Scholar
  12. 12.
    Turnbull, L., Samanta, B.: Cloud robotics: formation control of a multi robot system utilizing cloud infrastructure. In: Southeastcon 2013 Proceedings of IEEE, pp. 1–4, April 2013Google Scholar
  13. 13.
    Sefraoui, O., Aissaoui, M., Eleuldj, M.: Openstack: toward an open-source solution for cloud computing. Int. J. Comput. Appl. 55(3), 38–42 (2012)Google Scholar
  14. 14.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T.B., Leibs, J., Wheeler R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  15. 15.
    Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 2011Google Scholar
  16. 16.
    Open Perspective Foundation. Aligning object templates to a point cloud. Accessed 01 Apr 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Parkpoom Chaisiriprasert
    • 1
    Email author
  • Karn Yongsiriwit
    • 1
  • Apiporn Simapornchai
    • 2
  • Matthew Dailey
    • 2
  1. 1.College of Information and Communication TechnologyRangsit UniversityLak HokThailand
  2. 2.Computer Science and Information ManagementAsian Institute of TechnologyKhlong NuengThailand

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