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


Soyguder S.

Expert Systems with Applications, vol.38, no.11, pp.13994-14000, 2011 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 11
  • Publication Date: 2011
  • Doi Number: 10.1016/j.eswa.2011.04.207
  • Title of Journal : Expert Systems with Applications
  • Page Numbers: pp.13994-14000

Abstract

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. © 2011 Elsevier Ltd. All rights reserved.