發(fā)布時間：2019-04-15 18:38

【摘要】：隨著機器人技術(shù)的不斷發(fā)展,多移動機器人系統(tǒng)因其高運行效率、強魯棒性和廣泛的應(yīng)用領(lǐng)域受到了越來越多學者的關(guān)注和研究。能夠?qū)ξ粗獜碗s外界環(huán)境感知、建模并確定自身的位置,是多移動機器人自主導航的前提和基礎(chǔ)�，F(xiàn)實中的很多任務(wù)僅憑借傳統(tǒng)的定位問題難以解決,此時就要求將移動機器人定位與目標跟蹤方法結(jié)合起來。本文致力于解決這兩個相互耦合的問題:多移動機器人自身狀態(tài)估計和對于目標的狀態(tài)估計。重點圍繞協(xié)同定位和目標跟蹤兩項關(guān)鍵內(nèi)容展開研究,針對這兩項內(nèi)容分別建立了相應(yīng)的模型,提出了相應(yīng)的優(yōu)化算法,并對所提算法進行了仿真實驗,分析了實驗結(jié)果。多機器人協(xié)同定位部分,分析了多移動機器人協(xié)同系統(tǒng)的體系結(jié)構(gòu)和通信方法,針對單移動機器人在探索未知復雜環(huán)境時,存在魯棒性較差,效率較低等問題以及現(xiàn)有多機器人協(xié)同定位算法實時性、和定位精度較差等缺陷,提出基于平方根容積卡爾曼濾波的相對方位多機器人協(xié)同定位算法。利用相對方位作為測量值,在更新過程中直接傳遞目標狀態(tài)均值和協(xié)方差矩陣的平方根因子,精確度更高,更穩(wěn)定。計算均值和方差時采用基于容積準則的數(shù)值積分方法,降低了計算復雜度,實時性強。仿真實驗表明了該算法的精確性和有效性。多機器人協(xié)同目標跟蹤部分,針對移動機器人在未知復雜環(huán)境中動態(tài)目標追蹤存在的數(shù)值不穩(wěn)定、計算量大和精度較差等問題,提出基于平方根容積卡爾曼濾波的移動機器人動態(tài)目標跟蹤算法,該算法的系統(tǒng)狀態(tài)由地圖環(huán)境特征、機器人和目標作為一個整體構(gòu)成,通過數(shù)據(jù)關(guān)聯(lián)環(huán)節(jié)能夠有效的降低偽觀測值對系統(tǒng)狀態(tài)估計的影響。仿真結(jié)果表明了該算法的合理性和可操作性。針對未知環(huán)境下多機器人協(xié)同目標跟蹤問題,提出基于協(xié)方差交集的多機器人協(xié)同目標跟蹤算法。此算法具有分布式特點,在提高相關(guān)對象狀態(tài)估計準確性的同時,不必對數(shù)據(jù)信息進行獨立性假設(shè),避免了對象狀態(tài)間的互相關(guān)性估計,降低了系統(tǒng)通信能量損耗和計算復雜度。通過仿真實驗證明了該算法能夠有效解決未知環(huán)境下多機器人協(xié)同目標跟蹤問題。
[Abstract]:With the development of robot technology, the multi-mobile robot system has attracted more and more attention and research due to its high operating efficiency, strong robustness and wide range of applications. It is the premise and foundation of autonomous navigation for multi-mobile robot to be able to perceive the unknown and complex external environment, model and determine its own position. Many tasks in reality are difficult to solve only by traditional positioning problem, so it is necessary to combine mobile robot localization with target tracking method. In this paper, we focus on solving these two coupling problems: state estimation of multi-mobile robot and state estimation of target. Focusing on the two key contents of collaborative positioning and target tracking, the corresponding models are established, and the corresponding optimization algorithms are proposed. Simulation experiments are carried out on the proposed algorithms and the results of the experiments are analyzed. In the part of multi-robot cooperative positioning, the architecture and communication method of multi-mobile robot cooperative system are analyzed. The robustness of single mobile robot in exploring unknown and complex environment is poor. Based on the problems of low efficiency, real-time performance and poor positioning accuracy of existing multi-robot cooperative localization algorithms, a relative azimuth multi-robot co-location algorithm based on square root volume Kalman filter is proposed. Using the relative azimuth as the measured value, the square root factor of the target state mean and covariance matrix is transferred directly during the renewal process. The accuracy is higher and the stability is more stable. When calculating the mean and variance, the numerical integration method based on the volume criterion is adopted, which reduces the computational complexity and has a strong real-time performance. Simulation results show that the algorithm is accurate and effective. In the part of multi-robot cooperative target tracking, aiming at the problems of dynamic target tracking of mobile robot in unknown and complex environment, such as numerical instability, large amount of computation and poor precision, and so on. A moving target tracking algorithm for mobile robot based on square root volume Kalman filter is proposed. The system state of the algorithm is composed of map environment feature, robot and target as a whole. The influence of pseudo-observation value on system state estimation can be effectively reduced by data correlation link. The simulation results show that the algorithm is reasonable and feasible. A multi-robot cooperative target tracking algorithm based on covariance intersection is proposed to solve the problem of multi-robot cooperative target tracking in unknown environment. This algorithm has distributed characteristics. While improving the accuracy of the state estimation of related objects, it does not need to assume the independence of the data information, thus avoiding the cross-correlation estimation between the states of the objects. The energy loss and computational complexity of the system are reduced. The simulation results show that the algorithm can effectively solve the problem of multi-robot cooperative target tracking in unknown environment.
【學位授予單位】：安徽工程大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

【參考文獻】

相關(guān)期刊論文 前10條

1 李朕陽;郎朗;陳孟元;;基于SR-CKF的移動機器人動態(tài)目標跟蹤算法[J];電子測量與儀器學報;2016年08期

2 李朕陽;郎朗;陳孟元;;基于SR-CKF的相對方位多機器人協(xié)同定位算法[J];電子測量與儀器學報;2016年07期

3 朱奇光;張朋珍;李昊立;詹賢嬌;陳穎;;基于全局和局部特征融合的圖像匹配算法研究[J];儀器儀表學報;2016年01期

4 王碩;宋申民;史小平;于m[;;噪聲特性未知的多傳感器協(xié)方差交叉融合姿態(tài)估計[J];控制與決策;2016年02期

5 付夢印;呂憲偉;劉彤;楊毅;李星河;李玉;;基于RGB-D數(shù)據(jù)的實時SLAM算法[J];機器人;2015年06期

6 王碧霞;李銀伢;戚國慶;盛安冬;;基于SR-UKF的純方位多移動機器人協(xié)同定位算法[J];南京理工大學學報;2015年04期

7 張霖;歐林林;俞立;;執(zhí)行能力有限的兩差動輪機器人目標跟蹤控制器[J];信息與控制;2015年02期

8 朱奇光;張興家;陳衛(wèi)東;陳穎;;基于平方根容積機器人蒙特卡羅定位算法研究[J];儀器儀表學報;2015年04期

9 任航;;基于擬蒙特卡洛濾波的改進式粒子濾波目標跟蹤算法[J];電子測量與儀器學報;2015年02期

10 張晗;陳衛(wèi)東;王景川;;多機器人探索系統(tǒng)的人機共享控制[J];機器人;2015年01期

相關(guān)碩士學位論文 前1條

1 李倜;基于鼠腦海馬認知機理的機器人面向目標的導航模型研究[D];北京工業(yè)大學;2016年

，

本文編號：2458383