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Vision-Based Online Trajectory Generation and Its Application to Catching

  • Akio Namiki
  • Masatoshi Ishikawa
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 4)

Abstract

In this paper, a method for vision-based online trajectory generation is proposed. The proposed method is based on a nonlinear mapping of visual information to the desired trajectory, and this nonlinear mapping is defined by learning based on constraints of dynamics and kinematics. This method is applied to a catching task, and a reactive and flexible motion is obtained owing to real-time high-speed visual information. Experimental results on catching a moving object using a high-speed vision chip system are presented.

Keywords

Visual Information Joint Angle Joint Torque Trajectory Generation Target Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Albus, J. (1991). Outline for a theory of intelligence. IEEE Trans. on Syst., Man, and Cybern., 21(3):473–509.CrossRefMathSciNetGoogle Scholar
  2. 2.
    Allen, P., Yoshimi, B., and Timucenko, A. (1991). Real-time visual servoing. Proc. IEEE Int. Conf. Robot. and Automat., pages 2376–2384.Google Scholar
  3. 3.
    Bard, C., Turrell, Y., Fleury, M., Teasdale, N., Lamarre, Y., and Martin, O. (1999). Deafferentation and pointing with visual double-step perturbations. Exp. Brain Res., 125:410–416.CrossRefGoogle Scholar
  4. 4.
    B. Ghosh, N. Xi, and T.J. Tarn (1999). Control in robotics and automation: sensor-based integration. ACADEMIC Press.Google Scholar
  5. 5.
    Büehler, M., Koditshek, D., and Kindlmann, P. (1994). Planning and control of robotic juggling and catching tasks. Int. J. of Robot. Res., 13(2):101–118.CrossRefGoogle Scholar
  6. 6.
    Hong, W. and Slotine, J. (1995). Experiments in hand-eye coordination using active vision. Proc. 4th Int. Symp. on Experimental Robot. Google Scholar
  7. 7.
    ai]Ishikawa, M., Morita, A., and Takayanagi, N. (1992). High speed vision system using massively parallel processing. Proc. IEEE Int. Conf. Intelligent Robot. and Systems, pages 373–377.Google Scholar
  8. 8.
    Komuro, T., Ishii, I., and Ishikawa, M. (1997). Vision chip architecture using general-purpose processing elements for 1ms vision system. Proc. IEEE Int. Workshop on Computer Architecture for Machine Perception, pages 276–279.Google Scholar
  9. 9.
    Kovio, A. and Houshangi, N. (1992). Real-time vision feedback for servoing robotic manipulator with self-tuning controller. IEEE Trans. of Syst., Man, and Cybern., 21(1):134–142.CrossRefGoogle Scholar
  10. 10.
    Nakabo, Y., Ishikawa, M., Toyoda, H., and Mizuno, S. (2000). 1ms column parallel vision system and its application of high speed target tracking. Proc. IEEE Int. Conf. Robot. and Automat., pages 650–655.Google Scholar
  11. 11.
    Namiki, A. and Ishikawa, M. (2001). Sensory-motor fusion architecture based on high-speed sensory feedback and its application to grasping and manipulation. Proc. Int. Symp. Robotics, pages 784–789.Google Scholar
  12. 12.
    Namiki, A., Nakabo, Y., Ishii, I., and Ishikawa, M. (1999a). 1ms grasping system using visual and force feedback. Video Proc. IEEE Int. Conf. Robot. Automat. Google Scholar
  13. 13.
    Namiki, A., Nakabo, Y., Ishii, I., and Ishikawa, M. (1999b). High speed grasping using visual and force feedback. Proc. IEEE Int. Conf. Robot. Automat., pages 3195–3200.Google Scholar
  14. 14.
    Namiki, A., Nakabo, Y., Ishii, I., and Ishikawa, M. (2000). 1ms sensory-motor fusion system. IEEE/ASME Trans. Mechatron., 5(3):244–252.CrossRefGoogle Scholar
  15. 15.
    Sakaguchi, T., Fujita, M., Watanabe, H., and Miyazaki, F. (1993). Motion planning and control for a robot performer. Proc. IEEE Int. Conf. Robot. and Automat., 3:925–931.Google Scholar
  16. 16.
    Zhang, M. and Buehler, M. (1994). Sensor-based online trajectctory genera-tion for smoothly grasping moving objects. Proc. IEEE Int. Symp. Intelligent Control, pages 141–146.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Akio Namiki
    • 1
  • Masatoshi Ishikawa
    • 1
  1. 1.University of TokyoTokyoJapan

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