本文選題：移動(dòng)機(jī)器人 + 視頻路徑��； 參考：《鄭州大學(xué)》2017年碩士論文

【摘要】：移動(dòng)機(jī)器人導(dǎo)航是機(jī)器人領(lǐng)域的一個(gè)研究熱點(diǎn)。視覺傳感器作為最接近人類的環(huán)境感知工具,具有其它傳感器所無(wú)法比擬的優(yōu)勢(shì)。本文針對(duì)移動(dòng)機(jī)器人視覺導(dǎo)航進(jìn)行了研究,考慮到機(jī)器人在導(dǎo)航上的成本、性價(jià)比與實(shí)時(shí)性,以單目視覺中雙視圖定位為基礎(chǔ),提出了一種基于單目視頻路徑的導(dǎo)航方法,并結(jié)合2D激光雷達(dá)進(jìn)行拓展,實(shí)現(xiàn)在未知環(huán)境下的避障。本文完成的導(dǎo)航工作主要包括:視覺定位、路徑規(guī)劃、自主避障三個(gè)方面。第一部分,視覺定位。本文以單目視覺中雙視圖定位為基礎(chǔ),引入?yún)⒖悸窂降囊曨l序列,從中提取有序的關(guān)鍵圖像。使用Harris角點(diǎn)算法提取圖像特征點(diǎn),利用零均值歸一化互相關(guān)算法進(jìn)行圖像匹配,根據(jù)當(dāng)前圖像和最近似關(guān)鍵圖像,匹配點(diǎn)之間的約束關(guān)系求出基礎(chǔ)矩陣并優(yōu)化,得到它們的位姿參數(shù),進(jìn)而完成移動(dòng)機(jī)器人定位。第二部分,路徑規(guī)劃。移動(dòng)機(jī)器人在視頻路徑的指導(dǎo)下,判斷當(dāng)前圖像與關(guān)鍵圖像共有的特征點(diǎn)集的形心橫坐標(biāo)位置,以形心橫坐標(biāo)為參照,進(jìn)行視覺路徑跟蹤,使移動(dòng)機(jī)器人沿參考路徑行走,實(shí)現(xiàn)機(jī)器人的路徑規(guī)劃。第三部分,自主避障。本文采用激光雷達(dá)測(cè)距,基于柵格地圖進(jìn)行避障控制建模,通過(guò)定義避障風(fēng)險(xiǎn)函數(shù)、碰撞函數(shù)、運(yùn)動(dòng)速度、移動(dòng)曲率等一系列參數(shù),將避障過(guò)程化繁為簡(jiǎn),分為三個(gè)階段實(shí)施,實(shí)現(xiàn)移動(dòng)機(jī)器人在上述導(dǎo)航過(guò)程中的自主避障。最后,在真實(shí)的環(huán)境下,基于自主研發(fā)的科研平臺(tái),對(duì)所提出的導(dǎo)航方法進(jìn)行了驗(yàn)證,證明該方法在室外環(huán)境中具有較好的實(shí)用性。
[Abstract]:Mobile robot navigation is a research hotspot in robot field. As the closest environmental sensing tool, visual sensor has the advantage that other sensors can't compare. In this paper, the vision navigation of mobile robot is studied. Considering the cost, cost performance and real time of robot navigation, a navigation method based on monocular video path is proposed based on biview positioning in monocular vision. Combined with 2D lidar, the obstacle avoidance is realized in unknown environment. The navigation work accomplished in this paper mainly includes three aspects: visual positioning, path planning and autonomous obstacle avoidance. The first part, visual positioning. Based on the biview localization in monocular vision, this paper introduces the video sequence of reference path to extract the ordered key image. Harris corner algorithm is used to extract image feature points, and zero-mean normalized cross-correlation algorithm is used to match the image. According to the constraint relation between the current image and the most recent key image, the basic matrix is obtained and optimized. The position and pose parameters are obtained, and then the mobile robot positioning is completed. The second part, path planning. Under the guidance of the video path, the mobile robot judges the position of the centroid coordinate of the feature point set shared by the current image and the key image, and makes the mobile robot walk along the reference path by using the centroid and transverse coordinate as the reference, so that the mobile robot can walk along the reference path. The robot path planning is realized. The third part, autonomous obstacle avoidance. In this paper, the model of obstacle avoidance control based on grid map is established by using lidar ranging. By defining a series of parameters, such as obstacle avoidance risk function, collision function, motion velocity and moving curvature, the process of obstacle avoidance is simplified and divided into three stages. The autonomous obstacle avoidance of the mobile robot in the above navigation process is realized. Finally, in the real environment, based on the independent research platform, the proposed navigation method is verified, and it is proved that the method has good practicability in outdoor environment.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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