基于改进蜉蝣优化算法的机器人磁定位方法

Parking Positioning Method for Automatic Guided Vehicle Based on MA-LM Algorithm

针对现有物流机器人自主导航停车充电方案在远距离时停车定位精度差,造成自动回充模式下移动机器人无法对准充电桩的难题,提出一种基于改进蜉蝣优化算法(MA-LM)的物流机器人停车定位方法。本文方法将多个磁传感器阵列的磁钉定位数据融合,提高物流机器人停车定位的定位精度和定向精度。为了量化评估磁钉定位的提升效果,本文方法使用9个磁传感器组成的传感器阵列和两轮差速移动机器人在充电桩场景测试。相较于遗传优化算法和粒子群优化算法,本文提出的MA-LM算法的定位精度具有优势,在停车定位环节使用MA-LM算法的物流机器人达到定位精度±1.65 mm和定向精度0.9°。

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.