An Application of Laser-Based Autonomous Navigation for Data-Center Monitoring

  • Stefano Rosa
  • Ludovico Orlando Russo
  • Giuseppe Airó Farulla
  • Luca Carlone
  • Roberto Antonini
  • Gaspardone Marco
  • Basilio Bona
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Data-center monitoring has been a critical subject of research in recent years. Mobile robots have been successfully employed in the industrial field to efficiently perform common tasks. In this paper, we report some preliminary results on the study and development of a robotic system, in which a mobile robot equipped with a laser range sensor and an Inertial Measurement Unit (IMU) is able to autonomously navigate in a data-center room for accurate monitoring of critical measurements, such as servers’ external temperature, humidity and other physical quantities. The robot is able to autonomously create a map of a previously unknown room, localize therein and execute a list of measurements at different locations, which are provided by the user via a web graphical user interface (GUI). The robot is able to find the best trajectory to reach the given locations, while avoiding static and moving obstacles. The particular characteristics of the data-center scenario introduce specific problems related to map creation and localization using laser-based techniques (e.g., irregular surfaces as metal grids and high symmetry of the environment), which must be properly taken into account and are discussed throughout the paper. Preliminary experimental results show that the system is able to create a consistent map of the environment, to correctly localize itself therein and to follow a given path.

Keywords

Mobile robotics Data-center monitoring SLAM Localization Path planning 

References

  1. 1.
    Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Probabilistic robotics. MIT press, 2005.Google Scholar
  2. 2.
    Fabrizio Abrate et al. “Cooperative robotic teams for supervision and management of large logistic spaces: Methodology and applications”. In: Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on. IEEE. 2010, pp. 1–8.Google Scholar
  3. 3.
    Thomas Ristenpart et al. “Hey, you, get off my cloud: exploring information leakage in third-party compute clouds”. In: Proceedings of the 16th ACM conference on Computer and communications security. ACM. 2009, pp. 199–212.Google Scholar
  4. 4.
    Jonathan Koomey. “Growth in data center electricity use 2005 to 2010”. In: The New York Times 49.3 (2011).Google Scholar
  5. 5.
    Michael K Patterson. “The effect of data center temperature on energy efficiency”. In: Thermal and Thermomechanical Phenomena in Electronic Systems, 2008. ITHERM 2008. 11th Intersociety Conference on. IEEE. 2008, pp. 1167–1174.Google Scholar
  6. 6.
    Kenneth G Brill. Data center energy efficiency and productivity. 2007.Google Scholar
  7. 7.
    Chandrakant D Patel et al. “Computational fluid dynamics modeling of high compute density data centers to assure system inlet air specications”. In: Proceedings of IPACK. Vol. 1. 2001, pp. 8–13.Google Scholar
  8. 8.
    Chandrakant D Patel et al. “Smart cooling of data centers”. In: ASME 2003 International Electronic Packaging Technical Conference and Exhibition. American Society of Mechanical Engineers. 2003, pp. 129–137.Google Scholar
  9. 9.
    Parthasarathy Ranganathan et al. “Ensemble-level power management for dense blade servers”. In: ACM SIGARCH Computer Architecture News. Vol. 34. 2. IEEE Computer Society. 2006, pp. 66–77.Google Scholar
  10. 10.
    Cullen E Bash, Chandrakant D Patel, and Ratnesh K Sharma. “Dynamic thermal management of air cooled data centers”. In: Thermal and Thermomechanical Phenomena in Electronics Systems, 2006. ITHERM’06. The Tenth Intersociety Conference on. IEEE. 2006, 8–pp.Google Scholar
  11. 11.
    Ripal Nathuji, Canturk Isci, and Eugene Gorbatov. “Exploiting platform heterogeneity for power efficient data centers”. In: Autonomic Computing, 2007. ICAC’07. Fourth International Conference on. IEEE. 2007, pp. 5–5.Google Scholar
  12. 12.
    Rajarshi Das et al. “Autonomic multi-agent management of power and performance in data centers”. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track. International Foundation for Autonomous Agents and Multiagent Systems. 2008, pp. 107–114.Google Scholar
  13. 13.
    Luca Parolini, Bruno Sinopoli, and Bruce H Krogh. “Reducing data center energy consumption via coordinated cooling and load management”. In: Proceedings of the 2008 conference on Power aware computing and systems, HotPower. Vol. 8. 2008, pp. 14–14.Google Scholar
  14. 14.
    Hendrik F Hamann et al. “Methods and techniques for measuring and improving data center best practices”. In: Thermal and Thermomechanical Phenomena in Electronic Systems, 2008. ITHERM 2008. 11th Intersociety Conference on. IEEE. 2008, pp. 1146–1152.Google Scholar
  15. 15.
    Hendrik F Hamann et al. “Uncovering energy-efficiency opportunities in data centers”. In: IBM Journal of Research and Development 53.3 (2009), pp. 10–11.Google Scholar
  16. 16.
    Woong Choi, Ki-Woong Park, and Kyu Ho Park. “SCOUT: Data center monitoring system with multiple mobile robots”. In: Networked Computing and Advanced Information Management (NCM), 2011 7th International Conference on. IEEE. 2011, pp. 150–155.Google Scholar
  17. 17.
    Jonathan Lenchner et al. “Towards data center self-diagnosis using a mobile robot”. In: Proceedings of the 8th ACM international conference on Autonomic computing. ACM. 2011, pp. 81–90.Google Scholar
  18. 18.
    ROS (Robot Operating System). Website. http://www.ros.org.
  19. 19.
    F. Lu and E. Milios. “Globally consistent range scan alignment for environment mapping”. In: Autonomous Robots 4 (1997), pp. 333–349.Google Scholar
  20. 20.
    G. Grisetti, C. Stachniss, and W. Burgard. “Non-linear Constraint Network Optimization for Efficient Map Learning”. In: IEEE Trans. on Intelligent Transportation Systems 10.3 (2009), pp. 428–439.Google Scholar
  21. 21.
    R. Kummerle et al. “G2o: A general framework for graph optimization”. In: Robotics and Automation (ICRA), 2011 IEEE International Conference on. 2011, pp. 3607–3613. DOI:  10.1109/ICRA.2011.5979949.
  22. 22.
    Andrea Censi. “An ICP variant using a point-to-line metric”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, 2008. DOI:  10.1109/ROBOT.2008.4543181.
  23. 23.
    Luca Carlone et al. “Graph Optimization with Unstructured Covariance: Fast, Accurate, Linear Approximation”. In: Simulation, Modeling, and Programming for Autonomous Robots. Ed. by Itsuki Noda et al. Vol. 7628. Lecture Notes in Computer Science. Springer, Berlin Heidelberg, 2012, pp. 261–274. ISBN: 978-3-642-34326-1. DOI: 10.1007/978-3-642-34327-8_25.
  24. 24.
    D. Fox. “KLD-Sampling: Adaptive Particle Filters”. In: Advances in Neural Information Processing Systems 14. MIT Press, 2001.Google Scholar
  25. 25.
    I. Ulrich and J. Borenstein. “VFH+: reliable obstacle avoidance for fast mobile robots”. In: Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on. Vol. 2. 1998, 1572–1577 vol. 2. DOI: 10.1109/ROBOT.1998.677362.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stefano Rosa
    • 1
  • Ludovico Orlando Russo
    • 1
  • Giuseppe Airó Farulla
    • 1
  • Luca Carlone
    • 2
  • Roberto Antonini
    • 3
  • Gaspardone Marco
    • 3
  • Basilio Bona
    • 1
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTurinItaly
  2. 2.College of ComputingGeorgia Institute of TechnologyAtlantaGeorgia
  3. 3.Telecom ItaliaRomeItaly

Personalised recommendations