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)


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.


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

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