Inertial optimization MCL deep mine localization algorithm based on grey prediction and artificial bee colony

Abstract

To solve the problem that the existing Monte Carlo Localization (MCL) algorithm has long localization time and large localization error in the real-time localization of downhole personnel and mobile equipment, an inertial optimization MCL deep mine localization algorithm based on gray prediction and artificial bee colony (IMCL-GABC) is proposed. Firstly, the movement speed and direction of the personnel or equipment to be located at the current moment are estimated by the grey prediction model, and the sampling area is determined by combining with the structural characteristics of the deep mine roadway. Secondly, the artificial bee colony algorithm is introduced to optimize the filtering to eliminate the less likely position points and obtain the approximate optimal estimated position sampling set. Finally, the weight of the sample is optimized by motion inertia, so as to complete the localization of the personnel or mobile equipment to be located. The simulation results show that the average localization error of the IMCL-GABC algorithm is 0.46 m and the average localization time required for the node to move one step is 0.21 s. Compared with the other two mobile node localization algorithms MCL and Monte Carlo localization Boxed, the localization error of IMCL-GABC algorithm is reduced by 50% and 37.84% respectively, and the localization time is reduced by 4.6 s and 0.93 s respectively, which proves that IMCL-GABC algorithm effectively improves the localization accuracy and efficiency of downhole personnel and mobile equipment.