本文選題：機器人　切入點：未知環(huán)境　出處：《東北師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：機器人進入一個未知環(huán)境完成智能化任務(wù)時,需要感知和熟悉環(huán)境,此時就需要對環(huán)境進行建模和深層次的理解。這個過程可以通過建立環(huán)境地圖,并對環(huán)境地圖進行語義標注,形成語義地圖來實現(xiàn)。一個詳細、準確、深層次的語義地圖是機器人能否快速準確地實現(xiàn)日后智能化任務(wù)的前提和關(guān)鍵。構(gòu)建地圖的關(guān)鍵是獲取機器人自身的定位信息,但機器人要準確地獲取自身定位信息又要基于一個準確的構(gòu)建好的環(huán)境地圖。那么如何使機器人在未知自己位置和未構(gòu)建環(huán)境地圖時,能夠綜合利用自身對于位置信息的估計情況以及自身攜帶的環(huán)境感應(yīng)傳感器數(shù)據(jù),實現(xiàn)準確的地圖構(gòu)建成為機器人地圖研究的重點與難點。本文主要是圍繞構(gòu)建語義地圖的具體過程進行研究的,總體上可以分成三部分:未知環(huán)境的地圖構(gòu)建、語義地圖構(gòu)建和基于語義地圖的任務(wù)規(guī)劃。所做工作如下:綜合分析了現(xiàn)有機器人搭載的傳感器和一些不可避免的運動誤差后,提出并實現(xiàn)了2種基于機器人行位推測的地圖構(gòu)建方法,并利用它們準確快速地構(gòu)建出未知環(huán)境的2維地圖。在已建環(huán)境地圖的基礎(chǔ)上,利用機器人機載的語音模塊,通過人機語音對話的方式使機器人獲取環(huán)境中的物品語義信息,建立環(huán)境的語義地圖,從而解決了機器人感知和熟悉環(huán)境并構(gòu)建語義地圖的問題。服務(wù)機器人接收用戶對它發(fā)出的指令,通過語音識別和分詞操作,提取出指令中的物品關(guān)鍵詞和地點關(guān)鍵詞,并應(yīng)用路徑規(guī)劃算法,在構(gòu)建好的語義地圖上規(guī)劃出到達目標地點的路徑,完成給定的任務(wù)。
[Abstract]:When a robot enters an unknown environment to complete an intelligent task, it needs to be aware of and familiar with the environment, and then it needs to model and understand the environment at a deeper level. This process can be done by building an environment map. And carries on the semantic annotation to the environment map, forms the semantic map to realize. A detailed, accurate, The deep semantic map is the premise and key for the robot to realize the intelligent task quickly and accurately, and the key to construct the map is to obtain the localization information of the robot itself. But if the robot wants to get its own location information accurately, it must build a good environment map based on an accurate one, so how to make the robot know its own location and not build the environment map, The ability to synthetically utilize its own estimation of position information as well as the environmental sensor data it carries, The realization of accurate map construction has become the focus and difficulty of robot map research. This paper mainly focuses on the process of constructing semantic map, which can be divided into three parts as a whole: map construction of unknown environment, Semantic map construction and task planning based on semantic map. The work is as follows: after synthesizing the sensors and some inevitable motion errors of the existing robot, In this paper, two methods of map construction based on robot row prediction are proposed and implemented. Using them, 2D map of unknown environment is constructed accurately and quickly. On the basis of built environment map, the airborne speech module of robot is used. The robot acquires the semantic information of the objects in the environment and establishes the semantic map of the environment by the way of man-machine voice dialogue. In order to solve the problem of the robot perceiving and familiarizing the environment and constructing the semantic map, the service robot receives the instruction from the user and extracts the key words of the object and the place from the instruction by voice recognition and word segmentation operation. The path planning algorithm is used to plan the path to the target location on the constructed semantic map to complete the given task.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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