Advertisement

Application of RBFNN for Humanoid Robot Real Time Optimal Trajectory Generation in Running

  • Xusheng Lei
  • Jianbo Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

In this paper, a method for trajectory generation in running is proposed with Radial Basis Function Neural Network, which can generate a series of joint trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.

Keywords

Optimal Trajectory Radial Basis Function Neural Network Humanoid Robot Biped Robot Trajectory Generation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T.: The Development of Honda Humanoid Robot. In: IEEE Int. Conf. Robotics and Automation, Belgium, pp. 1321–1326 (1998)Google Scholar
  2. 2.
    Jessica, K., Hodgins, K.: Three-Dimensional Human Running. In: IEEE Int. Conf. On Robotics and Automation, Minesota, pp. 3271–3276 (1996)Google Scholar
  3. 3.
    Hyon, S.H., Rmura, T.: Aerial Posture Control for 3D Biped Running Using Compensator Around Yaw Axis. In: IEEE Int. Conf. On Robotics and Automation, Taiwan, pp. 57–62 (2003)Google Scholar
  4. 4.
    Kwon, O., Park, J.H.: Gait Transitions for Walking and Running of Biped Robots. In: IEEE Int. Conf. On Robotics and Automation, Taiwan, pp. 1350–1355 (2003)Google Scholar
  5. 5.
    Kajita, S., Nagasaki, T., Yokoi, K., Kaneko, K., Tanie, K.: Running Pattern Generation and Its Evaluation Using A Realistic Humanoid Model. In: IEEE Int. Conf. On Robotics and Automation, Taiwan, pp. 1336–1342 (2003)Google Scholar
  6. 6.
    Capi, G., Nasu, Y., Barolli, L., Mitobe, K.: Real Time Gait Generation for Autonomous Humanoid Robots: A Case Study for Walking. Robotics and Autonomous Systems 42, 107–116 (2003)zbMATHCrossRefGoogle Scholar
  7. 7.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks 2, 302–309 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xusheng Lei
    • 1
  • Jianbo Su
    • 1
  1. 1.Department of Automation & Research Center of Intelligent RoboticsShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations