Application and Simulation of GRNN for the Shotcrete Robot

摘要

The generalized regression neural network-one kind of RBF neural network, is chosen to construct the inverse-kinematics model for the shotcrete robot which has redundant degree-of–freedom. The inverse-kinematics model of the object is trained by the general learning method. In constructing model process, different partition methods is tried to divide the joint space and different diffusion coefficient value to train the neural network. The influence of the spread coefficient to the approach ability is also studied. The simulation method is adopted to test the performance of the neural network. The simulation result turns out to be satisfactory.