發(fā)布時(shí)間：2018-02-11 22:02

  本文關(guān)鍵詞： 虛擬試驗(yàn) 雙目視覺(jué) 物理引擎 作物行識(shí)別 導(dǎo)航控制　出處：《中國(guó)農(nóng)業(yè)大學(xué)》2017年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：農(nóng)機(jī)視覺(jué)導(dǎo)航系統(tǒng)在復(fù)雜田間環(huán)境中的靈活性較好,如何設(shè)計(jì)圖像處理算法以提高導(dǎo)航線(xiàn)識(shí)別精度和速度是當(dāng)前研究熱點(diǎn)之一。農(nóng)機(jī)導(dǎo)航系統(tǒng)的傳統(tǒng)試驗(yàn)方法以田間試驗(yàn)為主,存在試驗(yàn)成本較高、對(duì)作物生長(zhǎng)時(shí)期依賴(lài)性較強(qiáng)、試驗(yàn)周期較長(zhǎng)、試驗(yàn)過(guò)程易于對(duì)作物造成損傷等問(wèn)題。為解決上述問(wèn)題,本文以拖拉機(jī)為作業(yè)機(jī)械、苗期棉花為目標(biāo)作物、棉田作物行環(huán)境為試驗(yàn)場(chǎng)景,在虛擬現(xiàn)實(shí)環(huán)境下,研究拖拉機(jī)雙目視覺(jué)導(dǎo)航試驗(yàn)方法,主要研究?jī)?nèi)容如下:(1)虛擬導(dǎo)航試驗(yàn)場(chǎng)景幾何建模方法研究。根據(jù)實(shí)際田間試驗(yàn)環(huán)境的幾何特點(diǎn),建立拖拉機(jī)外觀(guān)模型、棉花單體模型、雜草模型、作物行模型和路面模型,形成虛擬試驗(yàn)場(chǎng)景的幾何模型。該方法能夠根據(jù)試驗(yàn)要求模擬多種棉田作物行場(chǎng)景,為研究作物行識(shí)別方法和路徑跟蹤控制方法提供豐富的試驗(yàn)環(huán)境。(2)拖拉機(jī)物理引擎建模方法研究。建立物理引擎的數(shù)學(xué)模型,包括整車(chē)模型及輪胎模型、傳動(dòng)系模型、制動(dòng)系模型、轉(zhuǎn)向系模型和解算模型。基于C++語(yǔ)言在Visual Studio 2008軟件環(huán)境下開(kāi)發(fā)物理引擎的軟件系統(tǒng)。該方法能夠根據(jù)實(shí)車(chē)參數(shù)和試驗(yàn)場(chǎng)景信息快速、正確地解算拖拉機(jī)的動(dòng)力學(xué)參數(shù),并在虛擬試驗(yàn)場(chǎng)景中實(shí)時(shí)渲染拖拉機(jī)的位姿狀態(tài)。(3)基于雙目視覺(jué)的田間導(dǎo)航路徑識(shí)別方法研究。通過(guò)檢測(cè)并統(tǒng)計(jì)作物行特征點(diǎn)的空間分布規(guī)律識(shí)別作物行中心線(xiàn),有效減少了圖像匹配點(diǎn)的數(shù)量,提高了識(shí)別精度和速度。在非地頭環(huán)境下,作物行中心線(xiàn)的正確識(shí)別率不小于92.11%,平均偏差角度的絕對(duì)值不大于1.07°,偏差角度的標(biāo)準(zhǔn)差不大于2.52°;圖像處理時(shí)間的平均值不大于202.90 ms、標(biāo)準(zhǔn)差不大于17.75 ms。通過(guò)比較作物行中心線(xiàn)與拖拉機(jī)行駛方位的相對(duì)位置規(guī)劃導(dǎo)航路徑,能夠保證拖拉機(jī)穩(wěn)定跟蹤同一條目標(biāo)作物行,目標(biāo)路徑規(guī)劃的正確率為97.33%;導(dǎo)航路徑規(guī)劃時(shí)間的平均值為0.017 ms,標(biāo)準(zhǔn)差為 0.017 ms。(4)拖拉機(jī)路徑跟蹤控制方法研究。基于純追蹤方法建立前輪轉(zhuǎn)向角計(jì)算模型。基于增量式PID算法設(shè)計(jì)轉(zhuǎn)向控制方法,運(yùn)用遺傳算法優(yōu)化PID控制器參數(shù)。設(shè)計(jì)路徑跟蹤控制策略以適應(yīng)不同類(lèi)型目標(biāo)路徑的跟蹤精度和速度要求。虛擬試驗(yàn)結(jié)果表明,該方法設(shè)計(jì)的路徑跟蹤控制系統(tǒng)能夠快速、穩(wěn)定地跟蹤目標(biāo)路徑,拖拉機(jī)的行駛軌跡相對(duì)于目標(biāo)路徑的超調(diào)量較小。(5)拖拉機(jī)虛擬導(dǎo)航系統(tǒng)驗(yàn)證試驗(yàn)。開(kāi)展臺(tái)階障礙、隨機(jī)路面和轉(zhuǎn)向性能虛擬試驗(yàn),測(cè)試物理引擎的有效性。分別運(yùn)用虛擬棉田作物行圖像和實(shí)際棉田作物行圖像測(cè)試作物行識(shí)別方法的性能。開(kāi)展平行階躍直線(xiàn)路徑和傾斜直線(xiàn)路徑的跟蹤虛擬試驗(yàn),測(cè)試路徑跟蹤控制系統(tǒng)的性能。開(kāi)展直線(xiàn)作物行和曲線(xiàn)作物行的跟蹤虛擬試驗(yàn),測(cè)試拖拉機(jī)虛擬導(dǎo)航試驗(yàn)系統(tǒng)的有效性。
[Abstract]:The flexibility of agricultural machinery visual navigation system in complex field environment is good. How to design image processing algorithm to improve the accuracy and speed of navigation line recognition is one of the current research hotspots. The traditional test method of agricultural machinery navigation system is mainly field experiment. In order to solve the above problems, such as high test cost, strong dependence on crop growth period, long test period and easy damage to crops, this paper takes tractor as working machine and seedling cotton as target crop. Under the virtual reality environment, the test method of binocular vision navigation for tractor is studied. The main research contents are as follows: (1) the geometric modeling method of virtual navigation test scene. According to the geometric characteristics of the actual field test environment, the tractor appearance model, cotton monomer model, weed model, crop row model and road surface model are established. The geometric model of virtual experiment scene is formed. In order to study crop row identification method and path tracking control method, this paper provides a rich test environment for tractor physical engine modeling, and establishes mathematical models of physical engine, including vehicle model, tire model, transmission system model, etc. Braking system model, steering system model and calculation model. Based on C language, the software system of physical engine is developed in Visual Studio 2008 software environment. Correctly calculate the dynamic parameters of the tractor, The field navigation path recognition method based on binocular vision is studied. By detecting and counting the spatial distribution of crop row feature points, the crop row centerline is identified. Effectively reduces the number of image matching points, improves the recognition accuracy and speed. The correct recognition rate of the crop line centerline is not less than 92.11, the absolute value of the average deviation angle is not more than 1.07 擄, the standard deviation of the deviation angle is not more than 2.52 擄, the average image processing time is not more than 202.90 Ms, and the standard deviation is not more than 17.75 Ms. Planning the navigation path of the relative position of the line centerline and the driving direction of the tractor, To ensure that the tractor keeps track of the same target crop row, The accuracy of target path planning is 97.33, the average time of navigation path planning is 0.017 ms, and the standard deviation is 0.017 ms.4) the method of tractor path tracking control is studied. Based on pure tracking method, the calculation model of front wheel steering angle is established. Quantitative PID algorithm is used to design steering control method. Genetic algorithm is used to optimize the parameters of PID controller. A path tracking control strategy is designed to meet the requirements of tracking accuracy and speed of different types of target paths. The virtual test results show that the path tracking control system designed by this method can be used quickly. Tracking the target path stably, the tractor track is smaller than the target path overshoot, the tractor virtual navigation system verification test. Step obstacle, random road surface and steering performance virtual test, To test the effectiveness of the physical engine, the performance of the crop row recognition method was tested by using the virtual crop row image and the actual cotton crop row image, respectively. The virtual experiment of parallel step straight path and inclined straight line path was carried out. To test the performance of the path tracking control system, to test the effectiveness of the tractor virtual navigation test system, the tracking virtual test of linear crop row and curve crop row was carried out.
【學(xué)位授予單位】：中國(guó)農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.9;S219

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