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Robotic Imitation of Human Hand Using RGB Color Gradient Mapping

  • T. ThyagarajEmail author
  • Kumar Abhishek
  • N. S. Brunda
  • Rakesh Ranjan
  • Nikita Kini
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 394)

Abstract

The state-of-the-art developments in robotics reveal a scenario wherein human motion imitation has been developed through advancements in features and performance of sensors. Most of such robots uses flex sensors to acquire the information about the orientation of the body being imitated. However, these are slow and are highly vulnerable to damages and physical changes. There are image processing algorithms that are focused on a robots learning through ANN and are not into imitation without memory. This paper presents an overview of research attempts to use a robotic assembly to imitate a real object using image processing. The paper describes results in force, tactile and visual sensing, sensor-based control, and the configuration of a test-based robot integrated system for imitation of a human hand, using vision through image processing using RGB scheme; and one-on-one mapping for the acquisition of the current position of a body and orientation of the various parts of the robot in same fashion. This research has resulted in the development of a prototype capable of imitation and its applications are in the areas of medicine, defense, safety, and explorations for both research and commercial applications.

Keywords

Robotic vision Color mapping Median filtering Robot-arm Embedded module image processing Robotics Robotic arm Human imitation Motion tracking Motion sensing Robotic vision Sensor-based robots Flex sensors Embedded systems Integrated systems Artificial neural networks Matlab RGB scheme 

References

  1. 1.
    Boucenna S, Anzalone S, Tilmont E, Cohen D, Chetouani M. Learning of social signatures through imitation game between a robot and a human partner. IEEE Trans Auton Mental Dev. 2014;6(3):213–25.Google Scholar
  2. 2.
    Calinon S, Billard A. Stochastic gesture production and recognition model for a humanoid robot. Intell Rob Syst (IROS 2004). Proc IEEE/RSJ Int Conf. 2004;3:2769–74.Google Scholar
  3. 3.
    Ijspeert AJ, Nakanishi J, Schaal S. Movement imitation with nonlinear dynamical systems in humanoid robots. Robotics and automation. Proceedings. ICRA ‘02. IEEE Int Conf. 2002;2:1398–1403. doi: 10.1109/ROBOT.2002.1014739.
  4. 4.
    Berthouze L, Bakker P, Kuniyoshi Y. Learning of oculo-motor control: a prelude to robotic imitation. Intelligent Robots and Systems ‘96, IROS 96. Proc IEEE/RSJ Int Conf. 1996;1:376–81. doi: 10.1109/IROS.1996.570702.
  5. 5.
    Zhang X, Lee MH. A developmental robot vision system”, systems, man and cybernetics, SMC ‘06. IEEE Int Conf. 2006;3:2024–29.Google Scholar
  6. 6.
    Fitch J, Coyle EJ, Gallagher NC Jr. Median filtering by threshold decomposition. IEEE Trans Acoust Speech Signal Proc. 1984;32(6):1183–88. doi: 10.1109/TASSP.1984.1164468.Google Scholar
  7. 7.
    Chan RH, Ho C-W, Nikolova M. Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Proc. 2005;14(10):1479–85. doi: 10.1109/TIP.2005.852196.Google Scholar
  8. 8.
    Guichard F, Morel JM. “Introduction to partial differential equations in image processing”. Tutorial Notes, IEEE Int. Conf. Image Proc., Washington. 1995.Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • T. Thyagaraj
    • 1
    Email author
  • Kumar Abhishek
    • 1
  • N. S. Brunda
    • 1
  • Rakesh Ranjan
    • 1
  • Nikita Kini
    • 1
  1. 1.Department of Electronics and Communication Engineering BMSITVisveshvaraya Technological University (VTU), All India Council for Technical Education (AICTE)KarnatakaIndia

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