發(fā)布時間：2018-03-24 12:40

  本文選題：地圖創(chuàng)建　切入點：ROS　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：近年來,移動機器人在各方面的應(yīng)用日益廣泛,與之相關(guān)的技術(shù)已在國內(nèi)外機器人領(lǐng)域掀起研究熱潮。對環(huán)境的認識和定位從而實現(xiàn)自主導(dǎo)航是移動機器人智能化的重要標志和特征。未知環(huán)境下的即時定位與地圖構(gòu)建(SLAM)一直是移動機器人技術(shù)領(lǐng)域的研究熱點。本項目面向室內(nèi)服務(wù)機器人系統(tǒng),開展基于ROS的移動機器人建圖導(dǎo)航相關(guān)技術(shù)研究,具體包括基于ROS的移動機器人操作系統(tǒng)開發(fā)、室內(nèi)環(huán)境自主探索建圖、以及局部路徑規(guī)劃等方面,具體研究內(nèi)容如下。首先,設(shè)計了移動機器人的硬件結(jié)構(gòu),完成機器人的傳感器等硬件系統(tǒng)搭建。在軟件控制系統(tǒng)方面,基于模塊化、分布式計算的設(shè)計思想,開發(fā)了基于ROS的移動機器人系統(tǒng)平臺,對移動機器人的傳感器信息采集、移動控制、建圖導(dǎo)航及環(huán)境觀測等節(jié)點進行了設(shè)計,最終搭建了一套完整的建圖導(dǎo)航機器人系統(tǒng)。該機器人能夠進行實時的地圖創(chuàng)建與導(dǎo)航,同時具備較好的人機交互能力。其次,針對目前SLAM系統(tǒng)中狀態(tài)估計無跡卡爾曼濾波算法(UKF)計算量大的問題,提出了基于局部采樣的UKF狀態(tài)估計算法。根據(jù)算法中狀態(tài)向量的特性,在UKF算法的UT采樣過程中采用僅對部分與當前狀態(tài)估計相關(guān)數(shù)據(jù)進行采樣的策略,降低了狀態(tài)估計的計算復(fù)雜度。對新的采樣算法進行公式推導(dǎo)分析,并通過仿真分析,驗證了提出的算法在保證濾波定位精度的同時降低計算量,提高機器人建圖的實時性。提出了基于全局路徑的改進動態(tài)窗口(DWA)局部路徑規(guī)劃算法。首先通過對傳統(tǒng)的DWA算法進行公式分析及仿真實驗研究,針對其評價函數(shù)中存在非必需項和特殊環(huán)境導(dǎo)航效果差的問題,提出了基于全局路徑規(guī)劃的DWA算法,并通過MATLAB實驗仿真分析證明改進方法的有效性與魯棒性。最后,利用搭建的移動機器人實驗平臺對機器人的自主探索SLAM進行了實驗,驗證了基于局部采樣的UKF定位算法和改進的DWA局部路徑規(guī)劃的算法的可行性。
[Abstract]:In recent years, mobile robots have been widely used in various fields. The related technologies have aroused a research boom in the field of robotics at home and abroad. Understanding and locating the environment to realize autonomous navigation is an important sign and feature of the intelligent mobile robot. Map building (slam) has always been a hot topic in the field of mobile robot technology. Research on mobile robot mapping and navigation technology based on ROS is carried out, including the development of mobile robot operating system based on ROS, autonomous exploration and mapping of indoor environment, and local path planning and so on. The specific research contents are as follows. The hardware structure of the mobile robot is designed, and the hardware system such as the sensor of the robot is built. In the aspect of software control system, the platform of mobile robot system based on ROS is developed based on the design idea of modularization and distributed computing. The sensor information collection, mobile control, map building and navigation and environment observation of mobile robot are designed. Finally, a complete mapping navigation robot system is built. The robot can create and navigate maps in real time. At the same time, it has good human-computer interaction ability. Secondly, aiming at the problem that the state estimation unscented Kalman filter algorithm in SLAM system is computationally large, A state estimation algorithm for UKF based on local sampling is proposed. According to the characteristics of the state vector in the algorithm, only part of the data related to the current state estimation is sampled in the UT sampling process of the UKF algorithm. The computational complexity of state estimation is reduced. The formula derivation and analysis of the new sampling algorithm are carried out, and the simulation results show that the proposed algorithm not only ensures the accuracy of filtering location, but also reduces the computational complexity. An improved dynamic window DWA-based local path planning algorithm based on global path is proposed to improve the real-time performance of robot mapping. Firstly, the traditional DWA algorithm is studied by formula analysis and simulation experiments. In order to solve the problem of non-essential item and poor navigation effect in special environment, the DWA algorithm based on global path planning is proposed, and the effectiveness and robustness of the improved method are proved by MATLAB simulation. Based on the experimental platform of mobile robot, the autonomous exploration SLAM of the robot is tested, and the feasibility of the UKF location algorithm based on local sampling and the improved DWA local path planning algorithm is verified.
【學(xué)位授予單位】：哈爾濱工業(yè)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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本文編號：1658333