發(fā)布時(shí)間：2018-08-30 10:28

【摘要】：隨著我國(guó)工業(yè)自動(dòng)化程度的提高,越來(lái)越多的工廠和倉(cāng)儲(chǔ)車間實(shí)現(xiàn)了智能化生產(chǎn)和運(yùn)輸,而不需要人工的參與。在這一過程中,自動(dòng)導(dǎo)引車(Automated Guided Vehicle,AGV)作為一種新型的智能運(yùn)輸裝置,起到了重要作用。本課題以此為背景進(jìn)行了基于激光雷達(dá)的室內(nèi)移動(dòng)機(jī)器人同時(shí)定位與室內(nèi)地圖構(gòu)建算法的研究。首先定義了機(jī)器人系統(tǒng)坐標(biāo)系,完成了基于里程計(jì)的運(yùn)動(dòng)模型和激光雷達(dá)環(huán)境感知模型的搭建,并根據(jù)激光雷達(dá)傳輸數(shù)據(jù)類型研究了激光雷達(dá)數(shù)據(jù)的讀取方法。通過對(duì)比論證了本課題采用柵格地圖作為地圖模型的可行性,完成了基于概率模型的柵格地圖狀態(tài)更新算法的研究,并用MATLAB驗(yàn)證了該算法的建圖效果。針對(duì)基于粒子濾波SLAM定位算法中因粒子退化導(dǎo)致位姿估計(jì)誤差較大的問題,本文以貝葉斯濾波理論為基礎(chǔ),通過粒子濾波定位算法的理論推導(dǎo),分析了粒子濾波過程中粒子退化的原因,針對(duì)該問題,完成了重采樣算法的研究,設(shè)計(jì)了基于不同重采樣算法的粒子濾波器,并完成仿真環(huán)境中的定位實(shí)驗(yàn),根據(jù)實(shí)驗(yàn)效果選擇了更適用于本課題的分層重采樣算法的粒子濾波器。在此基礎(chǔ)上,引入Rao-Blackwellized粒子濾波器(RBPF)的概念,并進(jìn)行了常規(guī)RBPF-SLAM算法的研究。在常規(guī)RBPF-SLAM算法框架的基礎(chǔ)上研究了混合提議分布的理論,并提出了基于分層重采樣的改進(jìn)RBPF-SLAM算法,通過編寫仿真程序利用公開數(shù)據(jù)集進(jìn)行仿真實(shí)驗(yàn),驗(yàn)證了算法改進(jìn)的有效性。以Turtlebot機(jī)器人為機(jī)器人實(shí)驗(yàn)平臺(tái),PC為上位機(jī),分別運(yùn)用原始的和改進(jìn)后的SLAM算法進(jìn)行真實(shí)環(huán)境中定位與建圖實(shí)驗(yàn),實(shí)驗(yàn)表明運(yùn)用改進(jìn)后的算法能夠得到更好的定位與建圖效果。最后完成算法的嵌入式移植,并結(jié)合AGV樣機(jī)進(jìn)行給定運(yùn)行軌跡下的定位與建圖,驗(yàn)證了算法在實(shí)際應(yīng)用中的有效性。
[Abstract]:With the improvement of industrial automation in China, more and more factories and warehousing workshops realize intelligent production and transportation without the need of manual participation. In this process, (Automated Guided Vehicle,AGV (automatic guidance vehicle) plays an important role as a new type of intelligent transportation device. In this paper, the algorithm of simultaneous localization and indoor map construction of indoor mobile robot based on lidar is studied. Firstly, the coordinate system of robot system is defined, the motion model based on odometer and the environment sensing model of lidar are built, and the reading method of laser radar data is studied according to the type of data transmitted by laser radar. The feasibility of using the raster map as the map model is compared and the research of the raster map status updating algorithm based on the probability model is completed. The effect of the algorithm is verified by MATLAB. In order to solve the problem of large position and orientation estimation error caused by particle degradation in SLAM location algorithm based on particle filter, this paper is based on Bayesian filtering theory and deduces the theory of particle filter localization algorithm. The reason of particle degradation in particle filter is analyzed. The resampling algorithm is studied, particle filter based on different resampling algorithm is designed, and the localization experiment in simulation environment is completed. According to the experimental results, the particle filter, which is more suitable for the layered resampling algorithm, is selected. On this basis, the concept of Rao-Blackwellized particle filter (RBPF) is introduced, and the conventional RBPF-SLAM algorithm is studied. Based on the conventional RBPF-SLAM algorithm framework, the theory of mixed proposal distribution is studied, and an improved RBPF-SLAM algorithm based on stratified resampling is proposed. The effectiveness of the improved algorithm is verified by writing a simulation program and using the open dataset to carry out simulation experiments. Using Turtlebot robot as the robot experimental platform and PC as the host computer, the original and improved SLAM algorithm are used for the real environment localization and map building experiment respectively. The experiment shows that the improved algorithm can get better localization and mapping effect. Finally, the embedded transplant of the algorithm is completed, and the location and construction of the AGV prototype under the given running trajectory are carried out, which verifies the effectiveness of the algorithm in practical application.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242;TN958.98

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張一棟;;用于自主移動(dòng)機(jī)器人的導(dǎo)航定位技術(shù)分析[J];集成電路應(yīng)用;2017年02期

2 李建勇;劉雪梅;李雪霞;杜博陽(yáng);;基于ROS的開源移動(dòng)機(jī)器人系統(tǒng)設(shè)計(jì)[J];機(jī)電工程;2017年02期

3 常皓;楊巍;;基于全向移動(dòng)模型的Gmapping算法[J];計(jì)量與測(cè)試技術(shù);2016年10期

4 張毅;鄭瀟峰;羅元;龐冬雪;;基于高斯分布重采樣的Rao-Blackwellized粒子濾波SLAM算法[J];控制與決策;2016年12期

5 羅元;蘇琴;張毅;鄭瀟峰;;基于優(yōu)化RBPF的同時(shí)定位與地圖構(gòu)建[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年05期

6 任福繼;孫曉;;智能機(jī)器人的現(xiàn)狀及發(fā)展[J];科技導(dǎo)報(bào);2015年21期

7 張永澤;艾長(zhǎng)勝;張尉;;慣導(dǎo)與視覺相結(jié)合的AGV小車控制系統(tǒng)設(shè)計(jì)[J];山東工業(yè)技術(shù);2015年20期

8 羅元;傅有力;程鐵鳳;;基于改進(jìn)Rao-Blackwellized粒子濾波器的同時(shí)定位與地圖構(gòu)建[J];控制理論與應(yīng)用;2015年02期

9 王忠立;趙杰;蔡鶴皋;;大規(guī)模環(huán)境下基于圖優(yōu)化SLAM的圖構(gòu)建方法[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2015年01期

10 王法勝;魯明羽;趙清杰;袁澤劍;;粒子濾波算法[J];計(jì)算機(jī)學(xué)報(bào);2014年08期

相關(guān)博士學(xué)位論文 前1條

1 曲麗萍;移動(dòng)機(jī)器人同步定位與地圖構(gòu)建關(guān)鍵技術(shù)的研究[D];哈爾濱工程大學(xué);2013年

相關(guān)碩士學(xué)位論文 前6條

1 李昀澤;基于激光雷達(dá)的室內(nèi)機(jī)器人SLAM研究[D];華南理工大學(xué);2016年

2 朱福利;基于SLAM的移動(dòng)機(jī)器人室內(nèi)環(huán)境感知和地圖構(gòu)建研究[D];廣東工業(yè)大學(xué);2016年

3 梁瀟;基于激光與單目視覺融合的機(jī)器人室內(nèi)定位與制圖研究[D];哈爾濱工業(yè)大學(xué);2016年

4 邵長(zhǎng)勉;動(dòng)態(tài)環(huán)境下移動(dòng)機(jī)器人定位及地圖創(chuàng)建[D];南京郵電大學(xué);2014年

5 武玫;基于分布式粒子濾波的SLAM算法研究[D];北京工業(yè)大學(xué);2013年

6 劉輕塵;基于激光測(cè)距儀的救援機(jī)器人二維地圖實(shí)時(shí)創(chuàng)建技術(shù)研究[D];哈爾濱工程大學(xué);2013年

，

本文編號(hào)：2212800