發(fā)布時(shí)間：2018-12-10 19:00

【摘要】：自動(dòng)導(dǎo)引車(Automated guided vehicle,AGV)在智能制造與物流系統(tǒng)中得到了快速的發(fā)展,其全局定位是自主導(dǎo)航技術(shù)中的研究熱點(diǎn)之一。馬爾可夫(Markov)定位算法是一種基于概率分布的全局定位方法,其通用性強(qiáng)并能解決多模和非線性問題,得到了廣泛的關(guān)注和研究。目前在特征地圖中還沒有適用的基于概率的全局定位方法,本文在建立AGV的Markov定位方法相關(guān)模型的基礎(chǔ)上,對(duì)特征地圖中的全局定位問題展開了研究。針對(duì)特征地圖中應(yīng)用Markov定位算法在對(duì)自動(dòng)導(dǎo)引車全局定位時(shí),常會(huì)出現(xiàn)傳感器觀測(cè)與地圖之間的特征數(shù)據(jù)關(guān)聯(lián)不唯一而導(dǎo)致定位失敗的問題。提出了一種不通過數(shù)據(jù)關(guān)聯(lián)的Markov定位計(jì)算新方法。利用高斯核函數(shù)將環(huán)境中的稀疏特征擬合成平滑致密曲線,通過對(duì)比傳感器觀測(cè)和算法預(yù)測(cè)得到的兩個(gè)致密曲線相似度來計(jì)算Markov定位中的觀測(cè)模型。同時(shí)直接利用電子羅盤傳感器得到AGV的姿態(tài)信息,使算法只關(guān)注于求解AGV離散化位置的信度而不必同時(shí)計(jì)算AGV位姿的三維數(shù)據(jù),在解決常規(guī)Markov定位方法在中心對(duì)稱環(huán)境中失效問題的基礎(chǔ)上減少了算法計(jì)算量,通過仿真分析驗(yàn)證了該全局定位方法的有效性。針對(duì)Markov定位算法計(jì)算量大、效率低的問題,在AGV姿態(tài)由電子羅盤信息直接得出的基礎(chǔ)上提出了基于四叉樹模型的變分辨率離散化平面柵格方法。通過將AGV位姿估計(jì)的三維狀態(tài)空間減少到二維平面柵格,并在定位過程中減少對(duì)地圖中信度常為零的柵格區(qū)域的重復(fù)計(jì)算以提高算法的運(yùn)算效率和信度極值收斂速度。仿真結(jié)果驗(yàn)證了變分辨率柵格離散化方法的有效性,與基于高斯核函數(shù)的固定分辨率Markov定位方法相比,該方法對(duì)AGV的全局定位效率更高。最后,通過半封閉環(huán)境下的自動(dòng)導(dǎo)引車全局定位實(shí)驗(yàn),對(duì)比擴(kuò)展卡爾曼濾波方法估計(jì)出的AGV運(yùn)動(dòng)軌跡,驗(yàn)證了本文方法即使在AGV初始位姿未知的情況下,估計(jì)出的AGV運(yùn)動(dòng)軌跡精度依舊更高,定位結(jié)果更有效。
[Abstract]:Automatic guided vehicle (Automated guided vehicle,AGV) has been developed rapidly in intelligent manufacturing and logistics system, and its global positioning is one of the research hotspots in autonomous navigation technology. Markov (Markov) localization algorithm is a global localization method based on probability distribution. It has strong generality and can solve multi-mode and nonlinear problems. At present, there is no probabilistic global localization method in feature map. Based on the establishment of AGV's Markov localization model, the global localization problem in feature map is studied in this paper. When the Markov algorithm is applied to the global localization of the automatic guided vehicle in the feature map, there is often a problem that the correlation between the sensor observation and the map is not unique, which leads to the failure of the location. A new method of Markov location calculation without data association is proposed. By using Gao Si kernel function, the sparse features in the environment are combined to form smooth dense curves, and the observation models in Markov location are calculated by comparing the similarity of the two dense curves obtained by sensor observation and algorithm prediction. At the same time, the attitude information of AGV is obtained by using the electronic compass sensor directly, so that the algorithm only focuses on solving the reliability of the discrete position of the AGV without having to calculate the three-dimensional data of the AGV position at the same time. On the basis of solving the failure problem of conventional Markov localization method in centrosymmetric environment, the computational complexity of the algorithm is reduced, and the effectiveness of the global localization method is verified by simulation analysis. Aiming at the problem of large computation and low efficiency of Markov localization algorithm, a variable resolution discrete plane grid method based on quadtree model is proposed based on the AGV attitude obtained directly from electronic compass information. By reducing the three-dimensional state space of AGV position and pose estimation to two-dimensional plane grid and reducing the repeated calculation of grid region where the information degree of map is usually zero in the process of location, the computational efficiency and the convergence speed of reliability extremum of the algorithm are improved. The simulation results verify the effectiveness of the variable resolution grid discretization method. Compared with the fixed resolution Markov location method based on Gao Si kernel function, this method is more efficient for global positioning of AGV. Finally, by comparing the AGV trajectory estimated by extended Kalman filter in semi-closed environment, it is verified that this method is even if the initial position and orientation of AGV are unknown. The estimated AGV trajectory accuracy is still higher and the localization result is more effective.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李延炬;肖宇峰;古松;賀希;郭正平;;基于激光傳感器的SLAM數(shù)據(jù)關(guān)聯(lián)算法的研究[J];微型機(jī)與應(yīng)用;2017年02期

2 李建雄;孟如;;智能倉儲(chǔ)機(jī)器人全局定位方法研究[J];工業(yè)控制計(jì)算機(jī);2016年09期

3 陳冠中;陳慶新;毛寧;俞愛林;張惠煜;;具有隨機(jī)路徑AGV的制造系統(tǒng)排隊(duì)網(wǎng)建模與分析[J];計(jì)算機(jī)集成制造系統(tǒng);2017年01期

4 華劍鋒;張豐;杜震洪;劉仁義;李榮亞;;基于變分辨率柵格模型的啟發(fā)式有向搜索最優(yōu)路徑算法[J];浙江大學(xué)學(xué)報(bào)(理學(xué)版);2016年01期

5 李聰波;肖衛(wèi)洪;杜彥斌;顧小進(jìn);穆安勇;;基于改進(jìn)ICP算法的損傷零部件精確配準(zhǔn)方法[J];計(jì)算機(jī)集成制造系統(tǒng);2016年04期

6 高巍;邵曉東;劉煥玲;;基于粒子濾波的自動(dòng)裝配定位方法[J];計(jì)算機(jī)集成制造系統(tǒng);2014年07期

7 吳玉秀;孟慶浩;曾明;;基于聲音的分布式多機(jī)器人相對(duì)定位[J];自動(dòng)化學(xué)報(bào);2014年05期

8 王煒;陳衛(wèi)東;王勇;;基于概率柵格地圖的移動(dòng)機(jī)器人可定位性估計(jì)[J];機(jī)器人;2012年04期

9 滿增光;葉文華;肖海寧;錢曉明;;從激光掃描數(shù)據(jù)中提取角點(diǎn)特征的方法[J];南京航空航天大學(xué)學(xué)報(bào);2012年03期

10 郭利進(jìn);師五喜;李穎;李福祥;;基于四叉樹的自適應(yīng)柵格地圖創(chuàng)建算法[J];控制與決策;2011年11期

相關(guān)博士學(xué)位論文 前2條

1 滿增光;基于激光雷達(dá)的室內(nèi)AGV地圖創(chuàng)建與定位方法研究[D];南京航空航天大學(xué);2014年

2 王衛(wèi)華;移動(dòng)機(jī)器人定位技術(shù)研究[D];華中科技大學(xué);2005年

相關(guān)碩士學(xué)位論文 前3條

1 李澤yN;基于輪廓掃描的自動(dòng)導(dǎo)引車定位與導(dǎo)引[D];哈爾濱工業(yè)大學(xué);2016年

2 郭麗曉;基于拓?fù)涞貓D的AGV智能路徑規(guī)劃技術(shù)研究[D];浙江大學(xué);2013年

3 閆宇航;改進(jìn)粒子濾波算法在FPGA中的研究與實(shí)現(xiàn)[D];北京交通大學(xué);2009年

，

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