Skip to main content

A Sensor Based Mechanism for Controlling Mobile Robots with ZigBee

  • Conference paper
  • First Online:
  • 1070 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 411))

Abstract

Mobile robots are now widely used in the daily life. A mobile robot is a one which allows motion in different directions and it can be used as a prototype in certain applications. By controlling the mobile robot using a sensor it can be used as a prototype for wheelchairs and thus they can assist the physically disabled people in their movement. This is very useful for them in their personnel as well as professional life. Thus the mobile robots are entered into the human day-to-day life. The sensor can capture the electrical impulses during the brain activity. And they are converted into commands for the movement of mobile robot. ZigBee is a wireless protocol used for the interaction between the computer and the mobile robot. Brain-computer interface is the communication system that enables the interaction between user and mobile robot. Electroencephalogram signals are used for controlling the mobile robots.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bi, L., Member, IEEE, Fan, X.-A., Liu, Y:, EEG-based brain-controlled mobile robots: a survey. IEEE Trans. Human-Mach. Syst. 43(2) (2013)

    Google Scholar 

  2. Arai, K., Mardiyanto, R.: Eyes based electric wheel chair control system. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 2(12) (2011)

    Google Scholar 

  3. Obermaier, B., Neuper, C., Guger, C., Associate Member, IEEE. In: Pfurtscheller, G.: Information transfer rate in a five-classes brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 9(3) (2001)

    Google Scholar 

  4. Philips, J., del R. Millán, J., Vanacker, G., Lew, E., Galán, F., Ferrez, P.W., Van Brussel, H., Nuttin, M.: Adaptive shared control of a brain-actuated simulated wheelchair. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, June 12–15, Noordwijk, The Netherlands

    Google Scholar 

  5. del R. Millán, J., Renkens, F., Mouriño, J., Student Member, IEEE, Gerstner, W.: Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 51(6) (2004)

    Google Scholar 

  6. Tanaka, K., Matsunaga, K., Wang, H.O.: Electroencephalogram-based control of an electric wheelchair. IEEE Trans. Robot. 21(4), 762–766 (2005)

    Google Scholar 

  7. Gandhi, V., Prasad, G., McGinnity, T.M., Coyle, D.H., Behera, L.: Intelligent adaptive user interfaces for BCI based robotic control. In: Proceedings of the Fifth International Brain-Computer Interface, Meeting 2013

    Google Scholar 

  8. Graimann, B., Allison, B., Pfurtscheller, G.: Brain-Computer Interfaces: A Gentle Introduction. Springer, Berlin (2010)

    Google Scholar 

  9. Hongbo, W.: Mobile Robot Positioning Based on ZigBee Wireless Sensor Networks and Vision Sensor

    Google Scholar 

  10. Gautam, G., Sumanth, G., Karthikeyan K.C., Sundar, S., Venkataraman, D.: Eye movement based electronic wheel chair for physically challenged persons. Int. J. Sci. Technol. Res. 3(2), 2014

    Google Scholar 

  11. Arora, A., Bhattacharyya, S.: An approach towards brain actuated control in the field of robotics using eeg signals: a review. In: International Conference of Advance Research and Innovation (ICARI-2014)

    Google Scholar 

  12. Gandhi, V., Prasad, G., Senior Member, IEEE, Coyle, D., Senior Member, IEEE, Behera, L., Senior Member, IEEE, McGinnity, T.M., Senior Member, IEEE: Quantum neural network-based EEG filtering for a brain–computer interface. IEEE Trans. Neural Netw. Learn. Syst. 25(2) (2014)

    Google Scholar 

  13. Chandra Jain, D.: A scenario of brain computer interaction with different types of face recognition techniques. Int. J. Comp. Sci. Eng. Tech. (IJCSET)

    Google Scholar 

  14. Jayabhavani, G.N., Raajan, N.R.: Brain enabled mechanized speech synthesizer using brain mobile interface. Int. J. Eng. Tech. (IJET)

    Google Scholar 

  15. Guger, C., Ramoser, H., Pfurtscheller, G.: Real-time EEG Analysis with Subject-Specific Spatial Patterns for a Brain-Computer Interface

    Google Scholar 

  16. Li, Y., Guan, C., Li, H., Chin, Z.: A self training semi-supervised SVM algorithm and its application in an EEG based brain computer interface speller system. Sci. Direct Pattern Recogn. Lett. 29 (2008)

    Google Scholar 

  17. Yanco, H.A., Drury, J.: Classifying Human-robot interaction: an updated taxonomy

    Google Scholar 

  18. Kuno, Y., Shimada, N., Shirai, Y.: A robotic wheelchair based on the integration of human and environmental observations. In: IEEE Robotics and Automation Magazine 26, March 2003

    Google Scholar 

  19. Yanco, H.A., Drury, J.L.: A Taxonomy for Human-Robot Interaction, AAAI Fall Symposium on Human-Robot Interaction, AAAI Technical Report FS-02-03., pp. 111–119, November 2002

    Google Scholar 

  20. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub§, E., Flor, H.: A spelling device for the paralysed, Macmillan Magazines Ltd, Nature, vol. 398, 25 March 1999

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sreena Narayanan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Narayanan, S., Divya, K.V. (2016). A Sensor Based Mechanism for Controlling Mobile Robots with ZigBee. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2731-1_1

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2729-8

  • Online ISBN: 978-81-322-2731-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics