Parking Positioning Method for Automatic Guided Vehicle Based on MA-LM Algorithm
To address the challenge that autonomous navigation parking and charging solutions have poor positioning accuracy at long distances,resulting in AGVs not being able to align with the charging pile in automatic charging back mode,a parking posi-tioning method based on an improved mayfly optimization algorithm(MA-LM)is proposed.This method fuses the magnetic nail positioning data from multiple magnetic sensor arrays,thereby improving the position accuracy and attitude accuracy of the park-ing positioning.To quantitatively evaluate the improvement effect of magnetic nail localization,this method is tested in a charg-ing pile scenario using a sensor array of nine magnetic sensors and a two-wheeled differential speed mobile robot.Compared with the genetic optimization algorithm(GA-LM)and the particle swarm optimization algorithm(PSO-LM),the experimental results show that the MA-LM algorithm has the localization accuracy of±1.65 mm and the orientation accuracy of 0.9°in the parking lo-calization.
automatic guided vehiclemayfly algorithmmagnetic nail navigationparking positioningcharging station navigation
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