DOI QR코드

DOI QR Code

Neural Learning-Based Inverse Kinematics of a Robotic Finger

뉴럴 러닝 기반 로봇 손가락의 역기구학

  • Kim, Byoung-Ho (Bio-mimetic Control & Robotics Lab., Div. of Electrical and Mechatronics Eng., Kyungsung Univ.)
  • 김병호 (경성대학교 전기전자메카트로닉스공학부 생체모방제어 및 로봇연구실)
  • Published : 2007.12.25

Abstract

The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

일반적으로 인간손에 있는 검지 손가락의 평면운동은 3개의 관절운동에 의해 이루어진다. 이러한 운동을 위해서는 기본적으로 역기구학 문제를 풀어야 하는데, 이것은 로봇 손을 이용한 파지나 조작행위에 있어서 필수적이다. 따라서 본 논문에서는 이러한 로봇 손가락의 역기구학 문제를 지능적으로 해결할 수 있는 뉴럴 러닝에 기반한 방법을 제안하고자 한다. 제안된 방법은 뉴럴 러닝에 있어서 동적인 학습율을 적용함으로써 보다 빠른 학습이 가능하고, 생체모방에 근거한 인간 손가락의 운동특성을 고려하는 것이 특징이다. 제안된 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행한다.

Keywords

References

  1. K. Hirai, M. Hirose, Y. Haikawa, and T. Takenaka, 'The development of Honda humanoid robot,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1321-1326, 1998
  2. S. Jacobsen, E. Iversen, D. Knutti, R. Jhonson, and K. Biggers, 'Design of the Utah/MIT dextrous hand,' Proc. 1986 IEEE Int. Conf. on Robotics and Automation, pp. 1520-1532, 1986
  3. C. S. Lovchik and M. A. Diftler, 'The Robonaut Hand: A dexterous robot hand for space,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 907-912, 1999
  4. J. L. Pons, R. Ceres, and F, Pfeiffer, 'Multifingered dexterous robotics hand design and control: a review,' Robotica, vol. 17, pp. 661-674, 1999 https://doi.org/10.1017/S0263574799001836
  5. J. Butterfass, M. Grebenstein, H. Liu, and G. Hirzinger, 'DLR-Hand II: Next generation of destrous robot hand,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 109-114, 2001
  6. M. R. Cutkosky, 'On grasp choice, grasp models, and the design of hands for manufacturing tasks,' IEEE Trans. on Robotics and Automation, vol. 5, no. 3, pp. 269-279, 1989 https://doi.org/10.1109/70.34763
  7. T. Iberall, 'Human prehension and dexterous robot hands,' Int. Jour. of Robotics Research, vol. 16, no. 3, pp.285-299, 1997 https://doi.org/10.1177/027836499701600302
  8. B.-H. Kim, 'A joint motion planning based on a biomimetic approach for human-like finger motion,' Int. Jour. of Control, Automation, and Systems, vol. 4, no. 2,pp. 217-226, 2006
  9. B.-H. Kim, 'A study on characteristics of interarticular coordination of human fingers for robotic hands,' Journal of the Korean Society of Precision Engineering, vol. 23, no. 7, pp. 67-75, 2006
  10. T. Yoshikawa, 'Analysis and control of robot manipulators with redundancy,' Robotics research: the first international symposium, Eds. M. Brady and R. Paul, Cambridge: MIT Press, pp. 735-747, 1984
  11. S. L. Chiu, 'Task compatibility of manipulator postures,' Int. Jour. of Robotics Research, vol. 7, no. 5, pp. 13-21, 1988 https://doi.org/10.1177/027836498800700502
  12. E. L. Secco, A. Visioli, and G. Magenes, 'Minimum jerk motion planning for a prothetic finger,' Jour. of Robotic Systems, vol. 21, no. 7, pp. 361-368, 2004 https://doi.org/10.1002/rob.20018
  13. C. C. Norkin and P. K. Levangie, Joint structure & function: a comprehensive analysis, F.A. Davis, 1992
  14. M. Nordin and V. H. Frankel, Basic biomechanics of the musculoskeletal system, Lippincott Williams & Wilkins press, pp. 358-387, 2001
  15. T. Wojitara and K. Nonami, 'Hand posture detection by neural network and grasp mapping for a master slave hand system,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 866-871, 2004
  16. P. Afshar and Y. Mastuoka, 'Neural-based control of a robotic hand: evidence for distinct muscle strategies,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 4633-4638, 2004
  17. S. S. Kim and S. Jung, 'Hardware implementation of a real time neural network controller with a DSP and an FPGA,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 4639-4644, 2004
  18. W.-K Choi, S.-H. Ha, S.-J. Kim, Y.-T. Kim and H.-T. Jeon, 'The intelligent control system for biped robot using hierachical mixture of experts,' Journal of Fuzzy Logic and Intelligent Systems, vol. 16, no. 4, pp. 389-395, 2006 https://doi.org/10.5391/JKIIS.2006.16.4.389
  19. J.-T. Ju, D.-W. Kim and K-B. Sim, 'Pattern classification algorithm of DNA chip image using ANN,' Journal of Fuzzy Logic and Intelligent Systems, vol. 16, no. 5, pp. 556-561, 2006 https://doi.org/10.5391/JKIIS.2006.16.5.556
  20. P. Hahn, H. Krimmer, A. Hradetzky, and U. Lanz, 'Quantitative analysis of the linkage between the interphalangeal joints of the index finger,' Jour. of Hand Surgery, vol. 20B, pp. 696-699, 1995
  21. D. G. Kamper, E. G. Cruz, and M. P. Siegel, 'Stereotypical fingertip trajectories during grasp,' Jour. of Neurophysiology, vol. 90, pp. 3702-3710, 2003 https://doi.org/10.1152/jn.00546.2003
  22. R. H. Nielsen, Neurocomputing, Addison-Wesley, 1990
  23. B.-H. Kim, 'Characteristics modeling of dynamic systems using adaptive neural computation,' Jour. of Control, Automation, and Systems, vol. 13, no. 4, pp. 309-314, 2007 https://doi.org/10.5302/J.ICROS.2007.13.4.309
  24. B. Massa, S. Roccella, M. C. Carrozza, and P. Dario, 'Design and development of an underactuated prosthtic hand,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 3374-3379, 2002

Cited by

  1. Design and Analysis of Ball Screw-driven Robotic Gripper vol.22, pp.1, 2012, https://doi.org/10.5391/JKIIS.2012.22.1.22