本文選題：預測裝置 + 位置姿態(tài)測量��； 參考：《西北農(nóng)林科技大學》2015年碩士論文

【摘要】：本文旨在研發(fā)肉牛產(chǎn)肉量活體預測裝置中的位置姿態(tài)測量模塊,測定預測裝置的位置與姿態(tài),為構(gòu)造牛體三維模型、進而預測活牛產(chǎn)肉量做準備。該模塊依據(jù)慣性導航原理,采用ZigBee無線傳輸技術(shù),通過采集的加速度和角速度數(shù)據(jù)計算預測裝置的位置與姿態(tài),主要工作如下:(1)設計實現(xiàn)位置姿態(tài)測量模塊。依據(jù)肉牛產(chǎn)肉量預測裝置的實際需求,從方便使用、低成本出發(fā)進行模塊設計,采用MEMS加速度計和陀螺及ZigBee模塊等構(gòu)建該模塊。ZigBee模塊選用CC2530控制加速度計和陀螺進行數(shù)據(jù)的采集,并完成數(shù)據(jù)的無線收發(fā),將數(shù)據(jù)傳送到PC機上進行處理。(2)建立加速度計和陀螺的誤差模型。分析加速度計和陀螺的誤差成分,構(gòu)建以零偏、刻度因子、交叉耦合項和隨機誤差為主的、含有24個待定參數(shù)的加速度計誤差模型和陀螺誤差模型。分別使用12位置法和角位置法,對加速度計誤差模型和陀螺誤差模型中的參數(shù)進行標定,導出誤差模型中的待定參數(shù),得到誤差模型的數(shù)學表達式。采用Kalman濾波算法對隨機誤差進行處理,降低加速度計和陀螺的隨機誤差。實驗表明,加速度計的測量誤差方差相對于使用誤差模型前的降低了84.78%;陀螺的測量誤差相對于使用誤差模型前的降低了88.25%。(3)解算載體的姿態(tài)和位置。根據(jù)采集到的加速度和角速度,采用四元數(shù)法先對姿態(tài)進行解算。使用四階龍格庫塔算法對四元數(shù)進行更新,以解算出的姿態(tài)信息為測量值,以采集的加速度數(shù)據(jù)為觀測值,使用UKF濾波算法進行數(shù)據(jù)融合,降低姿態(tài)解算過程中由陀螺漂移引起的誤差,300秒靜態(tài)姿態(tài)解算偏差在-0.2o~+0.2o以內(nèi),動態(tài)姿態(tài)解算偏差在-2o~+2o以內(nèi)。推導位置姿態(tài)測量模塊適用的比力方程,導出速度和位置的解算公式,位置解算長度為1m時,X軸最大誤差為0.0216m,Y軸最大誤差為0.0959m,Z軸最大誤差為-0.0289m。
[Abstract]:The purpose of this paper is to develop the position and attitude measurement module of the live prediction device for beef meat production, to determine the position and attitude of the prediction device, and to prepare for the construction of the three-dimensional model of cattle body and the prediction of live cattle meat production. According to the inertial navigation principle, ZigBee wireless transmission technology is used to calculate and predict the position and attitude of the device through the collected acceleration and angular velocity data. The main work is as follows: (1) Design and implement the position attitude measurement module. According to the actual demand of the meat quantity prediction device of beef cattle, the module is designed for convenience and low cost. The MEMS accelerometer, gyroscope and ZigBee module are used to construct the module. CC2530 control accelerometer and gyroscope are used to collect the data. The data is transferred to PC for processing. (2) the error model of accelerometer and gyroscope is established. The error components of accelerometers and gyroscopes are analyzed, and the error models of accelerometers and gyroscopes with 24 undetermined parameters are constructed, which are dominated by zero bias, scale factors, cross-coupling terms and random errors. The parameters of the accelerometer error model and the gyro error model are calibrated using the 12-position method and the angular position method respectively. The undetermined parameters in the error model are derived and the mathematical expressions of the error model are obtained. The Kalman filtering algorithm is used to deal with random errors to reduce the random errors of accelerometers and gyroscopes. The experimental results show that the error variance of accelerometer is 84.78 less than that before using the error model, and the measurement error of gyroscope is 88.25 lower than that before using error model. (3) the attitude and position of carrier are calculated. According to the collected acceleration and angular velocity, the quaternion method is used to calculate the attitude. The fourth order Runge-Kutta algorithm is used to update the quaternion. The attitude information is used as the measured value, the acceleration data is taken as the observation value, and the UKF filtering algorithm is used for data fusion. The error caused by gyroscope drift is reduced in the course of attitude calculation. The error of static attitude solution is within -0.2o- 0.2o, and the error of dynamic attitude solution is within -2o2o. The formula of velocity and position is derived. The maximum error of X axis is 0.0216mY axis when the length of position solution is 1 m, and the maximum error of Z axis is 0.0959mU Z axis is -0.0289mm.
【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S823;S818.9

【參考文獻】

相關(guān)碩士學位論文 前1條

1 張秋陽;無人機姿態(tài)測算及其誤差補償研究[D];中南大學;2011年

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本文編號：2088883