UKF-based ultrasonic network localization for a mine robot

Robot will play a significant role on unmanned coal mining in the future. According to the dark and confined characteristics in underground coal mine,an ultrasonic network localization technique based on unscented Kalman fil- ter (UKF) is proposed. The core of the method is to update and predict the robot position through UKF which com- bines the robot position output of light code disc and electronic compass localization with the robot position output of ultrasonic networks localization. Since the location update and prediction for a robot is a complex nonlinear function, the filtering accuracy is improved and the error of positioning is reduced effectively by the UKF. Since the nearer error of turning radius affects the turning radius more,the Sigmoid function is applied as the error coefficient to calculate the error sum of M turning radius. The robot turning radius error is reduced by adjusting the robot turning radius according to the error sum. The simulation results show that the more accurate and reliable location of robot is achieved with the proposed ultrasonic network localization based on UKF.