輪式移動機器人自主定位方法研究
發(fā)布時間：2018-11-17 18:09
【摘要】：準(zhǔn)確定位是移動機器人實現(xiàn)導(dǎo)航的前提,因此開展移動機器人的自主定位研究具有重要意義。本文對輪式移動機器人在室內(nèi)環(huán)境下的相對定位方法進行了系統(tǒng)地分析研究,主要內(nèi)容與結(jié)論如下：(1)搭建了輪式移動機器人實驗平臺,實驗證明該平臺能夠滿足本文擬定的實驗需求。為了分析該實驗平臺的航跡推測,建立了實驗平臺的誤差分析模型。(2)基于實驗平臺誤差分析模型,分析了輪徑與輪距對輪式移動機器人系統(tǒng)誤差的影響,并進行了UMBmark校核實驗。實驗結(jié)果表明UMBmark校核算法能有效校地核輪式移動機器人的系統(tǒng)參數(shù),且修正后的系統(tǒng)參數(shù)能夠明顯改善輪式移動機器人的位置估計精度。(3)基于實驗平臺誤差分析模型,分析了輪子打滑對輪式移動機器人隨機誤差的影響,并利用MEMS陀螺儀與編碼器的測量信息對實驗平臺直線運動時建立了輪子打滑模型,并給出了輪子打滑的判別式以及打滑后移動機器人實際位移的校核式。實驗結(jié)果表明該方法能準(zhǔn)確判別驅(qū)動輪是否打滑,同時對驅(qū)動輪打.滑校核后能有效提高輪式移動機器人的定位精度。(4)采用g-權(quán)重Hough變換與“平面有效區(qū)域”集合方法,實現(xiàn)了激光雷達(dá)測量與已存地圖的匹配,并運用最小二乘法對輪式移動機器人方向與位置誤差進行了校核。實驗結(jié)果表明該方法能夠有效補償位置估計誤差,尤其采用的點-點最小二乘法使自主定位更為準(zhǔn)確。(5)基于數(shù)據(jù)融合技術(shù),應(yīng)用擴展卡爾曼濾波(EKF)融合算法實現(xiàn)了航跡推測與激光雷達(dá)定位的融合。實驗結(jié)果表明該融合算法不僅對方向誤差和位置誤差具有良好的校核效果,而且能夠?qū)崿F(xiàn)位置的精確跟蹤。
[Abstract]:Accurate positioning is the premise of mobile robot navigation, so it is of great significance to carry out autonomous localization of mobile robot. In this paper, the relative positioning method of wheeled mobile robot in indoor environment is systematically analyzed and studied. The main contents and conclusions are as follows: (1) the experimental platform of wheeled mobile robot is built. Experiments show that the platform can meet the experimental requirements of this paper. In order to analyze the trajectory of the experimental platform, the error analysis model of the experimental platform is established. (2) based on the error analysis model of the experimental platform, the influence of wheel diameter and wheel distance on the system error of wheeled mobile robot is analyzed. The UMBmark verification experiment was carried out. The experimental results show that the UMBmark method can effectively calibrate the system parameters of the wheeled mobile robot, and the modified system parameters can obviously improve the accuracy of the position estimation of the wheeled mobile robot. (3) based on the error analysis model of the experimental platform, The influence of wheel skidding on the random error of wheeled mobile robot is analyzed, and the wheel skid model is established by using the measurement information of MEMS gyroscope and encoder for the linear motion of the experimental platform. The discriminant of wheel skidding and the checking of the actual displacement of mobile robot after skid are given. The experimental results show that the proposed method can accurately distinguish whether the driving wheel is sliding and at the same time beat the driving wheel. The accuracy of the wheeled mobile robot can be improved by sliding check. (4) the matching between the lidar measurement and the existing map is realized by using the g- weighted Hough transform and the "plane effective region" set method. The direction and position error of wheeled mobile robot is checked by least square method. The experimental results show that the proposed method can compensate the position estimation error effectively, especially the point-point least square method is used to make the autonomous location more accurate. (5) based on the data fusion technology, The extended Kalman filter (EKF) fusion algorithm is applied to realize the fusion of track estimation and lidar location. The experimental results show that the fusion algorithm not only has a good effect of checking the direction error and the position error, but also can accurately track the position.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP242
本文編號：2338652
[Abstract]:Accurate positioning is the premise of mobile robot navigation, so it is of great significance to carry out autonomous localization of mobile robot. In this paper, the relative positioning method of wheeled mobile robot in indoor environment is systematically analyzed and studied. The main contents and conclusions are as follows: (1) the experimental platform of wheeled mobile robot is built. Experiments show that the platform can meet the experimental requirements of this paper. In order to analyze the trajectory of the experimental platform, the error analysis model of the experimental platform is established. (2) based on the error analysis model of the experimental platform, the influence of wheel diameter and wheel distance on the system error of wheeled mobile robot is analyzed. The UMBmark verification experiment was carried out. The experimental results show that the UMBmark method can effectively calibrate the system parameters of the wheeled mobile robot, and the modified system parameters can obviously improve the accuracy of the position estimation of the wheeled mobile robot. (3) based on the error analysis model of the experimental platform, The influence of wheel skidding on the random error of wheeled mobile robot is analyzed, and the wheel skid model is established by using the measurement information of MEMS gyroscope and encoder for the linear motion of the experimental platform. The discriminant of wheel skidding and the checking of the actual displacement of mobile robot after skid are given. The experimental results show that the proposed method can accurately distinguish whether the driving wheel is sliding and at the same time beat the driving wheel. The accuracy of the wheeled mobile robot can be improved by sliding check. (4) the matching between the lidar measurement and the existing map is realized by using the g- weighted Hough transform and the "plane effective region" set method. The direction and position error of wheeled mobile robot is checked by least square method. The experimental results show that the proposed method can compensate the position estimation error effectively, especially the point-point least square method is used to make the autonomous location more accurate. (5) based on the data fusion technology, The extended Kalman filter (EKF) fusion algorithm is applied to realize the fusion of track estimation and lidar location. The experimental results show that the fusion algorithm not only has a good effect of checking the direction error and the position error, but also can accurately track the position.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP242
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 羅兵;張輝;吳美平;;微機械陀螺同步解調(diào)靈敏度分析[J];中國慣性技術(shù)學(xué)報;2010年02期
相關(guān)碩士學(xué)位論文 前10條
1 陳二磊;基于嵌入式系統(tǒng)的輪式機器人定位技術(shù)的研究與實現(xiàn)[D];西安電子科技大學(xué);2012年
2 張榮昌;輪式移動機器人機構(gòu)設(shè)計與運動控制研究[D];東北大學(xué);2011年
3 李堯;競速輪式移動機器人系統(tǒng)的開發(fā)研究[D];蘭州理工大學(xué);2011年
4 洪草根;基于AVR單片機的移動機器人控制系統(tǒng)設(shè)計[D];華北電力大學(xué);2011年
5 王兆密;基于MEMS陀螺的慣性測量單元的標(biāo)定與應(yīng)用[D];江蘇科技大學(xué);2011年
6 張劍光;基于傳感器信息權(quán)重的移動機器人導(dǎo)航控制[D];遼寧工程技術(shù)大學(xué);2011年
7 王慧;智能車的路徑識別與控制策略的研究[D];吉林大學(xué);2009年
8 于克;室內(nèi)自主移動機器人的定位研究[D];燕山大學(xué);2009年
9 張勇;基于多傳感器數(shù)據(jù)融合的清潔機器人定位技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2008年
10 梁大云;輪式與六桿機構(gòu)復(fù)合移動機器人機械系統(tǒng)研究[D];北京交通大學(xué);2008年
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