Abstract: Abstract： 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 positioning 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 parking positioning. To quantitatively evaluate the improvement effect of magnetic nail localization， this method is tested in a charging 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 localization.

Key words: Key words： automatic guided vehicle, mayfly algorithm, magnetic nail navigation, parking positioning, charging station navigation