【摘要】：針對使用二維碼作為定位模塊的視覺自動導引車(AGV)的軌跡跟蹤問題,提出了一種基于最優(yōu)偏差路徑的模糊PID糾偏算法。首先建立AGV的運動學方程,將橫向偏差和航向偏差作為控制系統(tǒng)的輸入變量;其次引入Hamilton最優(yōu)控制函數(shù),得到基于最優(yōu)偏差轉化策略的AGV最優(yōu)偏差路徑和最優(yōu)控制方程;最后以AGV與最優(yōu)偏差路徑之間的位姿偏差更新模糊PID控制器的參數(shù),實時調節(jié)驅動輪的差速,使AGV按最優(yōu)偏差路徑行駛,實現(xiàn)AGV糾偏的最優(yōu)控制。實驗結果表明,該方法可以平穩(wěn)、快速地消除橫向和航向偏差,本文控制方法在極端偏差狀態(tài)下的4種隸屬度區(qū)間的橫向偏差糾偏結果分別為2.38、2.54、3.29和4.43 mm,均不超過5 mm,糾偏距離小于1.2 m,跟蹤精度為3.2 mm,既提高無軌導引AGV的導航精度,也能較好地滿足系統(tǒng)運行的穩(wěn)定性和伺服驅動能力。
[Abstract]:Aiming at the trajectory tracking problem of visual automatic guided vehicle (AGV) using 2-D code as positioning module, a fuzzy PID deviation correction algorithm based on optimal deviation path is proposed. Firstly, the kinematics equation of AGV is established and the lateral deviation and heading deviation are regarded as the input variables of the control system, secondly, the Hamilton optimal control function is introduced to obtain the optimal deviation path and the optimal control equation of AGV based on the optimal deviation transformation strategy. Finally, the parameters of the fuzzy PID controller are updated by the position deviation between the AGV and the optimal deviation path, and the differential speed of the driving wheel is adjusted in real time to make the AGV travel according to the optimal deviation path so as to realize the optimal control of the AGV deviation correction. The experimental results show that the proposed method can eliminate the lateral and heading deviations smoothly and rapidly. The results of lateral deviation correction for the four membership ranges in this paper are 2.38,2.54, 3.29 and 4.43 mm, respectively under the condition of extreme deviation. The tracking accuracy of 3. 2 mm, can not only improve the navigation accuracy of trackless guidance AGV, but also satisfy the stability and servo drive ability of the system. The error correction distance is less than 5 mm, and the tracking accuracy is less than 1. 2m. and the tracking accuracy is 3. 2 mm,.
【作者單位】： 中國計量大學計量測試工程學院;
【基金】：國家自然科學基金(51675499) 浙江省自然科學基金(LY15E050013)項目資助
【分類號】：TP23

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本文編號：2463033