Intelligent control based on wavelet decomposition and neural network for predicting of human trajectories with a novel vision-based robotic

In this paper, an intelligent novel vision-based robotic tracking model is developed to predict the performance of human trajectories with a novel vision-based robotic tracking system. The developed model is based on wavelet packet decomposition, entropy and neural network. We represent an implementation of a novel vision-based robotic tracking system based on wavelet decomposition and artificial neural (WD-ANN) which can track desired human trajectory pattern in real environments. The input–output data set of the novel vision-based robotic tracking system were first stored and than these data sets were used to predict the robotic tracking based on WD-ANN. In simulations, performance measures were obtained to compare the predicted and human–robot trajectories like actual values for model validation. In statistical analysis, the RMS value is 0.0729 and the R2 value is 99.76% for the WD-ANN model. This study shows that the values predicted with the WD-ANN can be used to predict human trajectory by vision-based robotic tracking system quite accurately. All simulations have shown that the proposed method is more effective and controls the systems quite successful.