Localization Study of Mobile Robots Based on MFO-BP Algorithm
Aiming at the problem of mobile robot localization,based on an autonomous composite robot,a moth-flame optimization-back propagation(MFO-BP)algorithm-based is proposed as a prediction method for mobile robot localization.The mobile robot is regarded as a"black box",and the influence of systematic and non-systematic errors is not considered separately,the theoretical coordinate values are input,and the predicted coordinate values are output.The experimental results show that the MFO-BP algorithm prediction model can effectively predict the positioning of mobile robots,and the accuracy is much higher than that of the traditional back propagation(BP)neural network prediction model.To verify the influence of the model structure on the prediction results,the MFO-BP algorithm prediction model is divided into two kinds:single hidden layer and double hidden layer.The experimental results show that compared with the MFO-BP algorithm between double hidden layer and single hidden layer,the average absolute error of the former is smaller,the range of error fluctuation is also smaller,and the trend of prediction error is smoother.The prediction effect of the MFO-BP algorithm double hidden layer is better,and it can be applied to composite robots'end localization.
Mobile robotLocalizationPrediction modelMoth-flame optimization(MFO)algorithmBack-propagation(BP)neural networkHidden layer
万方数据
维普
