本文選題：導(dǎo)盲機(jī)器人 + 同時(shí)定位與地圖創(chuàng)建　； 參考：《江蘇科技大學(xué)》2016年碩士論文

【摘要】：近年來我國的視力障礙者數(shù)目不斷增長,因此,研發(fā)出一款能夠輔助視力障礙者行走的工具——導(dǎo)盲機(jī)器人是非常必要且迫切的。導(dǎo)盲機(jī)器人要實(shí)現(xiàn)在環(huán)境中自主運(yùn)行,一個(gè)不容忽視的前提就是建立環(huán)境地圖,并且能夠隨時(shí)在環(huán)境中完成自定位,也就是實(shí)時(shí)定位與地圖創(chuàng)建,本文的研究重點(diǎn)就是圍繞這個(gè)內(nèi)容展開的。本論文中以實(shí)驗(yàn)室的Turtlebot2為基礎(chǔ),研發(fā)了一種在ROS操作系統(tǒng)下,以無線手柄控制運(yùn)行,以激光測(cè)距儀進(jìn)行環(huán)境探測(cè)的導(dǎo)盲機(jī)器人實(shí)驗(yàn)樣機(jī)。研究內(nèi)容包括導(dǎo)盲機(jī)器人實(shí)驗(yàn)樣機(jī)的搭建,導(dǎo)盲機(jī)器人系統(tǒng)模型的構(gòu)建,導(dǎo)盲機(jī)器人SLAM算法研究與MATALB實(shí)驗(yàn)仿真,導(dǎo)盲機(jī)器人真實(shí)環(huán)境中實(shí)驗(yàn)及分析。首先,本文完成了導(dǎo)盲機(jī)器人樣機(jī)的搭建,將實(shí)驗(yàn)室turtlebot2機(jī)器人上的Kinect視覺傳感器更換為激光測(cè)距儀,作為機(jī)器人的外部觀測(cè)傳感器,同時(shí)將雙模無線震動(dòng)力反饋手柄與機(jī)器人連接,控制機(jī)器人的運(yùn)行。與此同時(shí),在ROS下,建立激光測(cè)距儀和手柄的通訊節(jié)點(diǎn),使得導(dǎo)盲機(jī)器人的各個(gè)節(jié)點(diǎn)間能夠相互通信。其次,對(duì)導(dǎo)盲機(jī)器人樣機(jī)和SLAM問題的基本思想進(jìn)行研究分析,建立導(dǎo)盲機(jī)器人運(yùn)動(dòng)模型、觀測(cè)模型、地圖模型等,其中,為簡化計(jì)算,導(dǎo)盲機(jī)器人在行走過程中采用的是直線型運(yùn)動(dòng)方式。然后,對(duì)導(dǎo)盲機(jī)器人實(shí)時(shí)定位與地圖創(chuàng)建的算法進(jìn)行研究,在卡爾曼濾波和粒子濾波算法的理論基礎(chǔ)上,依據(jù)文中建立的SLAM過程中的系統(tǒng)模型,采用基于擴(kuò)展卡爾曼濾波的EKF-SLAM算法和基于粒子濾波的FastSLAM算法,并利用MATLAB仿真軟件,創(chuàng)建導(dǎo)盲機(jī)器人運(yùn)行的仿真環(huán)境,分別進(jìn)行EKF-SLAM實(shí)驗(yàn)仿真和FastSLAM實(shí)驗(yàn)仿真,分析導(dǎo)盲機(jī)器人X方向,Y方向和航向角的位置誤差,實(shí)驗(yàn)仿真及對(duì)比結(jié)果顯示在各項(xiàng)參數(shù)基本相同的情況下,FastSLAM算法比EKF-SLAM算法的累積誤差要小。最后,本文利用自主搭建的導(dǎo)盲機(jī)器人實(shí)驗(yàn)平臺(tái),在三個(gè)真實(shí)環(huán)境中,構(gòu)建真實(shí)環(huán)境的二維地圖,分析實(shí)驗(yàn)中出現(xiàn)的問題,可以得出在創(chuàng)建地圖的過程中,地圖的質(zhì)量與機(jī)器人車體的穩(wěn)定性、機(jī)器人行走過程中打滑和環(huán)境的復(fù)雜程度有關(guān)。
[Abstract]:In recent years, the number of people with visual impairment has been increasing in our country, so it is necessary and urgent to develop a tool to assist the visually impaired people to walk-guided robot. In order to realize autonomous operation in the environment, a prerequisite that can not be ignored is the establishment of environmental map and the ability to complete self-localization at any time in the environment, that is, real-time location and map creation. This paper focuses on this content. In this paper, based on the laboratory Turtlebot2, an experimental prototype of a blind guided robot is developed, which operates under the ROS operating system, operates with a wireless handle and detects the environment with a laser rangefinder. The research contents include the construction of the experimental prototype of the guide robot, the construction of the system model of the guide robot, the research of the SLAM algorithm and the simulation of the MATALB experiment of the guide robot, and the experiment and analysis of the guide robot in the real environment. Firstly, the prototype of the blind robot is built, and the Kinect vision sensor on the lab turtlebot2 robot is replaced by the laser rangefinder, which is used as the external observation sensor of the robot. At the same time, the dual-mode wireless vibration force feedback handle is connected with the robot to control the operation of the robot. At the same time, the communication nodes of the laser rangefinder and the handle are established under ROS, so that the nodes of the robot can communicate with each other. Secondly, the basic ideas of the prototype and SLAM problem of the guided robot are studied and analyzed, and the motion model, observation model, map model and so on of the guided robot are established, among which, in order to simplify the calculation, In the course of walking, the blind robot adopts linear motion mode. Based on the theory of Kalman filter and particle filter, the system model of SLAM process is established. The EKF-SLAM algorithm based on extended Kalman filter and the FastSLAM algorithm based on particle filter are adopted, and the simulation environment of blind robot running is created by using MATLAB simulation software. The simulation of EKF-SLAM experiment and FastSLAM experiment are carried out, respectively. The position error of X direction and heading angle of blind robot is analyzed. Experimental simulation and comparison show that the accumulated error of fast slam algorithm is smaller than that of EKF-SLAM algorithm under the condition that the parameters are basically the same. Finally, using the self-built Blind Robot experiment platform, this paper constructs 2D map of real environment in three real environments, and analyzes the problems in the experiment, we can draw the conclusion that in the process of creating map, The quality of the map is related to the stability of the robot body, the slippage of the robot and the complexity of the environment.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP242
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本文編號(hào)：1877991