多通道融合的家用機器人精準定位研究
發(fā)布時間：2018-09-12 13:52
【摘要】：在普適計算的大背景下,隨著機器人技術(shù)的大力發(fā)展和人們生活質(zhì)量的提高,家用機器人的全球市場規(guī)模正在快速擴大。在家用機器人領(lǐng)域,定位技術(shù)是其中最基礎(chǔ)、最重要的一項研究內(nèi)容,研究人員在家用機器人定位方面做了很多研究工作.在家用機器人的實際應(yīng)用中,定位是首要的環(huán)節(jié)。為了使機器人的運動更加精確,本文主要做了以下幾個方面的工作。首先,分析了家用機器人常用的室內(nèi)定位技術(shù)的研究現(xiàn)狀,并分析了多通道融合的數(shù)據(jù)融合技術(shù)的研究現(xiàn)狀及當(dāng)前融合技術(shù)的技術(shù)難點。第二,在室內(nèi)WiFi定位技術(shù)方面,首先對定位的算法予以介紹,然后分析了常用的WiFi及RFID定位。在機器人的WiFi定位中,改進了一種模糊聚類融合算法的位置指紋定位法,相對于硬聚類算法,能夠有效提高室內(nèi)定位的定位精度。在機器人的RFID定位中,采用了將標簽分類的思想,實時提高定位的效率,并對定位的誤差進行了測試。第三,在運動學(xué)定位的研究中,對家用機器人的運動學(xué)進行了分析,首先分析了運動坐標系的模型理論,將運動學(xué)坐標系分為正運動模型和逆運動模型,然后對機器人的運動學(xué)定位中常用的超聲波定位和里程計定位的原理進行闡述,最后在試驗中完成了對里程計傳感器短時間內(nèi)定位的實驗,并對超聲波傳感器系統(tǒng)誤差的校正,并對超聲波的傳播距離進行了測試。在定位結(jié)果中顯示,對室內(nèi)定位的精度能夠滿足實驗需求。第四,在多通道的數(shù)據(jù)融合定位中,分析了DS證據(jù)理論,針對各通道獲取的數(shù)據(jù)的不確定性和證據(jù)間的相容性與互斥性,改進了一種基于二次調(diào)整加權(quán)的DS證據(jù)理論對定位的結(jié)果進行優(yōu)化,在程序中設(shè)置了標志位,根據(jù)獲取數(shù)據(jù)的來源判定標志位,在各通道均參與融合的情況下,將該方法與經(jīng)典DS證據(jù)理論的融合結(jié)果進行比較,結(jié)果表明,基于二次調(diào)整加權(quán)的DS證據(jù)理論能夠提高家用機器人的定位精度,滿足室內(nèi)定位的要求。
[Abstract]:Under the background of pervasive computing, with the rapid development of robot technology and the improvement of people's quality of life, the global market scale of home robot is expanding rapidly. In the field of home robot, localization technology is the most basic and the most important research content, researchers have done a lot of research work in the field of home robot location. In the practical application of home robot, positioning is the primary link. In order to make the robot motion more accurate, this paper mainly does the following work. Firstly, the research status of indoor positioning technology commonly used in home robot is analyzed, and the research status of multi-channel fusion data fusion technology and the technical difficulties of current fusion technology are analyzed. Secondly, in the aspect of indoor WiFi location technology, the location algorithm is introduced firstly, and then the commonly used WiFi and RFID localization are analyzed. In the WiFi localization of the robot, the location fingerprint location method of a fuzzy clustering fusion algorithm is improved. Compared with the hard clustering algorithm, it can effectively improve the positioning accuracy of indoor location. In the RFID localization of robot, the idea of classifying tags is adopted to improve the efficiency of localization in real time, and the error of positioning is tested. Thirdly, in the research of kinematics positioning, the kinematics of home robot is analyzed. Firstly, the model theory of kinematic coordinate system is analyzed, and the kinematics coordinate system is divided into forward motion model and inverse motion model. Then, the principle of ultrasonic positioning and mileage positioning, which is commonly used in kinematic positioning of robot, is expounded. Finally, the experiment of locating the odometer sensor in a short time is completed in the experiment, and the error correction of ultrasonic sensor system is also given. The propagation distance of ultrasonic wave was tested. The results show that the accuracy of indoor positioning can meet the experimental requirements. Fourthly, in the multi-channel data fusion positioning, the DS evidence theory is analyzed, aiming at the uncertainty of the data obtained by each channel and the compatibility and mutual exclusion among the evidence. In this paper, an improved DS evidence theory based on quadratic adjustment weights is proposed to optimize the localization results. A marker bit is set up in the program, and the symbol bit is determined according to the source of the acquired data. When all channels participate in the fusion, Compared with the fusion results of the classical DS evidence theory, the results show that the DS evidence theory based on quadratic adjustment and weighting can improve the positioning accuracy of the home robot and meet the requirements of indoor positioning.
【學(xué)位授予單位】：上海工程技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP242
[Abstract]:Under the background of pervasive computing, with the rapid development of robot technology and the improvement of people's quality of life, the global market scale of home robot is expanding rapidly. In the field of home robot, localization technology is the most basic and the most important research content, researchers have done a lot of research work in the field of home robot location. In the practical application of home robot, positioning is the primary link. In order to make the robot motion more accurate, this paper mainly does the following work. Firstly, the research status of indoor positioning technology commonly used in home robot is analyzed, and the research status of multi-channel fusion data fusion technology and the technical difficulties of current fusion technology are analyzed. Secondly, in the aspect of indoor WiFi location technology, the location algorithm is introduced firstly, and then the commonly used WiFi and RFID localization are analyzed. In the WiFi localization of the robot, the location fingerprint location method of a fuzzy clustering fusion algorithm is improved. Compared with the hard clustering algorithm, it can effectively improve the positioning accuracy of indoor location. In the RFID localization of robot, the idea of classifying tags is adopted to improve the efficiency of localization in real time, and the error of positioning is tested. Thirdly, in the research of kinematics positioning, the kinematics of home robot is analyzed. Firstly, the model theory of kinematic coordinate system is analyzed, and the kinematics coordinate system is divided into forward motion model and inverse motion model. Then, the principle of ultrasonic positioning and mileage positioning, which is commonly used in kinematic positioning of robot, is expounded. Finally, the experiment of locating the odometer sensor in a short time is completed in the experiment, and the error correction of ultrasonic sensor system is also given. The propagation distance of ultrasonic wave was tested. The results show that the accuracy of indoor positioning can meet the experimental requirements. Fourthly, in the multi-channel data fusion positioning, the DS evidence theory is analyzed, aiming at the uncertainty of the data obtained by each channel and the compatibility and mutual exclusion among the evidence. In this paper, an improved DS evidence theory based on quadratic adjustment weights is proposed to optimize the localization results. A marker bit is set up in the program, and the symbol bit is determined according to the source of the acquired data. When all channels participate in the fusion, Compared with the fusion results of the classical DS evidence theory, the results show that the DS evidence theory based on quadratic adjustment and weighting can improve the positioning accuracy of the home robot and meet the requirements of indoor positioning.
【學(xué)位授予單位】：上海工程技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP242
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相關(guān)期刊論文 前10條
1 呂吉爾;;越來越聰明的家用機器人[J];世界科學(xué);2009年10期
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