基于ROS的室內(nèi)移動服務(wù)機器人定位與導(dǎo)航系統(tǒng)的研究與開發(fā)
發(fā)布時間：2019-04-03 07:20
【摘要】：近年來,由于信息化、工業(yè)化的不斷推進,機器人產(chǎn)業(yè)在高新技術(shù)產(chǎn)業(yè)中不斷發(fā)展,正處于一個蓬勃興起的階段,在中國制造業(yè)進行轉(zhuǎn)型升級的浪潮中,機器人技術(shù)的發(fā)展尤其重要。2015年年末,北京國家會議中心舉辦了世界機器人大會,中國在戰(zhàn)略上也將機器人與智能制造納入了國家科技創(chuàng)新的優(yōu)先重點領(lǐng)域。機器人SLAM技術(shù)是機器人技術(shù)中核心技術(shù)之一,SLAM是即時定位與地圖構(gòu)建技術(shù),其中建圖定位與路徑規(guī)劃是SLAM算法主要的攻克方向。SLAM相關(guān)算法需要一個完整的通信系統(tǒng)將傳感器數(shù)據(jù)與算法結(jié)合。ROS又稱機器人操作系統(tǒng),它由斯坦福大學(xué)與Willow Garage公司共同研發(fā),ROS提供了一個標(biāo)準(zhǔn)的操作系統(tǒng)環(huán)境,可以實現(xiàn)硬件與PC中進程、PC中各進程間通信的功能,通過ROS可以將算法計算結(jié)果通過節(jié)點傳遞給底層控制層進行運動控制。圍繞上述幾個方面,本文圍繞機器人定位與路徑規(guī)劃兩個問題,在現(xiàn)有研究的基礎(chǔ)上,做出以下幾點工作:(1)研究機器人領(lǐng)域國內(nèi)外研究發(fā)展趨勢通過當(dāng)前相關(guān)科研進展來闡述本課題的意義,從當(dāng)前學(xué)術(shù)研究方向引出相關(guān)技術(shù)。(2)運動學(xué)模型及測量模型的研究對實驗平臺的速度模型與里程計模型進行研究并建模。同時分析機器人實際運行過程中傳感器可能受到的環(huán)境噪聲,對觀測模型進行研究,用于削減環(huán)境噪聲帶來的影響。(3)機器人定位算法本文所涉及的定位算法均是基于概率的算法,首先先根據(jù)機器人建立運動學(xué)模型及觀測模型,然后將模型融入于濾波算法中,實現(xiàn)相關(guān)位置估計算法,通過MATLAB來驗證算法有效性,并將算法融合到ROS中。(4)機器人導(dǎo)航算法導(dǎo)航算法分為全局與局部路徑規(guī)劃兩個方向。針對這兩個方向,本課題各提出兩種算法,并在MATLAB中驗證算法有效性,進而對路徑進行優(yōu)化處理,最終在ROS中實現(xiàn)對實驗平臺的控制。(5)整體實驗平臺搭建本文分別對定位與導(dǎo)航模塊進行MATLAB仿真并進行試驗平臺搭建,本課題使用雙輪車搭載Intel處理器、激光雷達等傳感器,通過STM32嵌入式開發(fā)板完成對小車的控制,ROS系統(tǒng)及算法在獨立小型低功耗PC上進行算法運算與數(shù)據(jù)交互,最終完成對實驗室環(huán)境的建圖定位與自主導(dǎo)航的任務(wù)。
[Abstract]:In recent years, due to the continuous advancement of informatization and industrialization, the robot industry is constantly developing in the high-tech industry and is in a flourishing stage. In the tide of transformation and upgrading of China's manufacturing industry, The development of robotics technology is particularly important. In late 2015, the Beijing National Convention Center held the World Robot Conference, and China has strategically included robotics and intelligent manufacturing in the priority areas of national scientific and technological innovation. Robot SLAM technology is one of the core technologies of robot technology, and SLAM is real-time positioning and map building technology. Mapping location and path planning are the main attack direction of SLAM algorithm. Lam correlation algorithm needs a complete communication system to combine sensor data and algorithm. Ros is also called robot operating system. Developed by Stanford University and Willow Garage, ROS provides a standard operating system environment that enables hardware to communicate with processes in PC and between processes in PC. Through ROS, the results of the algorithm can be passed to the bottom control layer for motion control. Around the above-mentioned aspects, this paper focuses on the robot positioning and path planning, on the basis of the existing research, The following work is done: (1) the research and development trend of robot field at home and abroad expounds the significance of this topic through the current related scientific research progress. The related technologies are derived from the current academic research direction. (2) the velocity model and odometer model of the experimental platform are studied and modeled by the research of kinematics model and measurement model. At the same time, the possible environmental noise of the sensor during the actual operation of the robot is analyzed, and the observation model is studied. It is used to reduce the impact of environmental noise. (3) the localization algorithms involved in this paper are all probability-based algorithms. Firstly, the kinematics model and observation model are established according to the robot. Then the model is incorporated into the filtering algorithm, and the correlation position estimation algorithm is implemented. The effectiveness of the algorithm is verified by MATLAB, and the algorithm is fused into ROS. (4) the robot navigation algorithm is divided into two directions: global and local path planning. In view of these two directions, two kinds of algorithms are proposed in this paper, and the validity of the algorithm is verified in MATLAB, and then the path is optimized. Finally, the control of the experiment platform is realized in ROS. (5) the whole experiment platform is built. In this paper, the positioning and navigation module is simulated by MATLAB and the test platform is built. In this paper, the dual-wheeled vehicle is used to carry on the Intel processor. Lidar and other sensors, through the STM32 embedded development board to complete the control of the car, ROS system and algorithm on the independent small low-power PC algorithm operation and data interaction, Finally, the task of mapping location and autonomous navigation of laboratory environment is completed.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
本文編號：2453020
[Abstract]:In recent years, due to the continuous advancement of informatization and industrialization, the robot industry is constantly developing in the high-tech industry and is in a flourishing stage. In the tide of transformation and upgrading of China's manufacturing industry, The development of robotics technology is particularly important. In late 2015, the Beijing National Convention Center held the World Robot Conference, and China has strategically included robotics and intelligent manufacturing in the priority areas of national scientific and technological innovation. Robot SLAM technology is one of the core technologies of robot technology, and SLAM is real-time positioning and map building technology. Mapping location and path planning are the main attack direction of SLAM algorithm. Lam correlation algorithm needs a complete communication system to combine sensor data and algorithm. Ros is also called robot operating system. Developed by Stanford University and Willow Garage, ROS provides a standard operating system environment that enables hardware to communicate with processes in PC and between processes in PC. Through ROS, the results of the algorithm can be passed to the bottom control layer for motion control. Around the above-mentioned aspects, this paper focuses on the robot positioning and path planning, on the basis of the existing research, The following work is done: (1) the research and development trend of robot field at home and abroad expounds the significance of this topic through the current related scientific research progress. The related technologies are derived from the current academic research direction. (2) the velocity model and odometer model of the experimental platform are studied and modeled by the research of kinematics model and measurement model. At the same time, the possible environmental noise of the sensor during the actual operation of the robot is analyzed, and the observation model is studied. It is used to reduce the impact of environmental noise. (3) the localization algorithms involved in this paper are all probability-based algorithms. Firstly, the kinematics model and observation model are established according to the robot. Then the model is incorporated into the filtering algorithm, and the correlation position estimation algorithm is implemented. The effectiveness of the algorithm is verified by MATLAB, and the algorithm is fused into ROS. (4) the robot navigation algorithm is divided into two directions: global and local path planning. In view of these two directions, two kinds of algorithms are proposed in this paper, and the validity of the algorithm is verified in MATLAB, and then the path is optimized. Finally, the control of the experiment platform is realized in ROS. (5) the whole experiment platform is built. In this paper, the positioning and navigation module is simulated by MATLAB and the test platform is built. In this paper, the dual-wheeled vehicle is used to carry on the Intel processor. Lidar and other sensors, through the STM32 embedded development board to complete the control of the car, ROS system and algorithm on the independent small low-power PC algorithm operation and data interaction, Finally, the task of mapping location and autonomous navigation of laboratory environment is completed.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
【引證文獻】
相關(guān)碩士學(xué)位論文 前2條
1 高日;基于多傳感器信息融合的機器人定位技術(shù)研究[D];北京建筑大學(xué);2018年
2 盧雙;多功能家庭床椅服務(wù)機器人研究[D];江南大學(xué);2018年
，本文編號：2453020
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