Advertisement

Location-Based Intelligent Robot Management Service Model Using RGPSi with AoA for Vertical Farm

  • Hong-Geun KimEmail author
  • Dae-Heon Park
  • Olly Roy Chowdhury
  • Chang-Sun Shin
  • Yong-Yun Cho
  • Jang-Woo Park
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)

Abstract

The purpose of this paper is that intelligent robot control service model is one of the service model for a complete auto control system in vertical farm. This model provides environment monitoring service based on context-aware without human intervention in vertical farm. These service need to provide necessary accurate locations indoor environment based on Ubiquitous Sensor Network (USN). It will precisely monitor crop growth environment by intelligent robot using the indoor localization algorithm based on Ratiometric Global Positioning System iteration (RGPSi) with Angle of Arrival (AoA). Therefore, this model makes it possible to shorten operate time and to decrease workforce in vertical farm. It also will contribute to the complete auto control system of vertical farm on a large scale.

Keywords

intelligent robot vertical farm control localization USN 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, Y.: International Technology Trends and Automation of Plant Factory. BION Special ZINE 18 (2010)Google Scholar
  2. 2.
    Lee, M.B., Kim, T., Kim, H.G., Bae, N.J., Baek, M., Park, J.W., Cho, Y.Y., Shin, C.S.: Implementation of the Closed Plant Factory System Based on Crop Growth Model. In: Park, J.J., Ng, J.K.-Y., Jeong, H.Y., Waluyo, B. (eds.) Multimedia and Ubiquitous Engineering. LNEE, vol. 240, pp. 83–89. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Kim, J., Im, J.: A Design of Intelligent Plant Factory Control Structure based on Ontology for Growth Environment. Korean Society for Internet Information 11(2), 107–108 (2010)CrossRefGoogle Scholar
  4. 4.
    Pack, R.T.: IMA: The Intelligent Machine Architecture. PhD thesis, Vanderbilt University, Nashville, Tennessee (2003)Google Scholar
  5. 5.
    Bouwer, A., Visser, A., Nack, F., Terwijn, B.: Location Awareness, Orientation and Navigation: Lessons Learned from the SmartInside Project. In: IUI Workshop on Location Awareness for Mixed and Dual Reality (2013)Google Scholar
  6. 6.
    Rodney, A.B.: A Robust Layered Control System for a Mobile Robot. IEEE J. of Robotics and Automation RA-2(1), 14–23 (1985)Google Scholar
  7. 7.
    Park, J.W., Kim, H.G.: Ratiometric GPS Iteration Localization Method Combined with the Angle of Arrival Measurement. International Journal of Smart Home 7(3), 197–205 (2013)Google Scholar
  8. 8.
    Kim, H., Lee, M., Kim, T., Bae, N., Beak, M., Shin, C., Cho, Y., Park, J.: The Efficient Indoor Localization Algorithm Through the Communication between IoT Devices. In: Conference on Korean Institute of Information & Telecommunication Facilities Engineering, pp. 27–32 (2012)Google Scholar
  9. 9.
    Imad, A., Cyril, R., Christophe, C.: Spatial models for context-aware indoor navigation system. Journal of Spatial Information Science 4, 85–123 (2012)Google Scholar
  10. 10.
    Lee, I.K., Kwon, S.H.: Ontology-based Control of Autonomous Robots. In: Korean Institute of Intelligent System Conference, vol. 19(1), pp. 69–74 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hong-Geun Kim
    • 1
    Email author
  • Dae-Heon Park
    • 2
  • Olly Roy Chowdhury
    • 1
  • Chang-Sun Shin
    • 1
  • Yong-Yun Cho
    • 1
  • Jang-Woo Park
    • 1
  1. 1.Department of Information and Communication EngineeringSunchon National UniversitySuncheonRepublic of Korea
  2. 2.Department of IoT ConvergenceElectronics and Telecommunications Research InstituteDaejeonRepublic of Korea

Personalised recommendations