Visual spherical robot modeling based on generalized regression neural network
Due to complex mechanical structure and special motion mode of the spherical robot,its dynamic model is characterized by nonlinear,multivariable,strong coupling,parameter uncertainty and other complex factors,so it is difficult to establish an accurate mathematical model.Aiming at the above problems,an improved generalized regression neural network(GRNN)is designed for modeling.Firstly,the measured data of the spherical robot based on the mechanism model are obtained.Then,an improved GRNN model is trained based on the measured data and its prediction effect is analyzed.Finally,the controller of the spherical robot is designed based on the improved GRNN and the mechanism model respectively for self-balancing experiments.The fluctuation amplitude of the former is smaller and the adjustment time is shorter than that of the latter by nearly 1 s.Experimental results show that the proposed modeling method is feasible and effective.
spherical robotvision devicemodeling of dynamicsgrey wolf optimization(GWO)algorithm
万方数据
维普
