Application of RBFNN for humanoid robot real time optimal trajectory generation in running
X Lei, J Su - International Symposium on Neural Networks, 2004 - Springer
X Lei, J Su
International Symposium on Neural Networks, 2004•SpringerIn 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.
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.
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.
Springer
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