Humanoid Robotics Modeling by Dynamic Fuzzy Neural Network

Abstract

Motion planning is an essential task for humanoid robots. However, it is still very challenging to obtain good motion performance in humanoid motion planning, because of its high DOFs (degree of freedoms), variable mechanical structure and nonlinearity. In humanoid motion planning, the motion performance can be given only after one whole cycle motion is completed. This is a demanding condition for motion planning on either real robots or simulation platform. In this paper, a DFNN (dynamic neural fuzzy network) is adopted to model humanoid robots for motion planning. The inputs of DFNN are parameters which determine motion of humanoid robots. The output is evaluation of humanoid motion performance. The DFNN after training can give evaluation of motion performance immediately once the parameters are determined. The DFNN models not only the dynamics of robots, also the motion planning method. Therefore, the DFNN stores two kind of knowledge: the mapping between parameters and humanoid motion, the mapping between humanoid motion and motion performance.