【摘要】：在無損檢測領(lǐng)域中,管道相貫線焊縫缺陷的檢測難度較大且檢測效率較低。通過磁性輪吸附在鋼管壁上的管道相貫線焊縫檢測機(jī)器人需要攜帶無損檢測設(shè)備沿著相貫線焊縫運(yùn)動(dòng)并對焊縫缺陷進(jìn)行檢測,檢測過程中還需要不斷調(diào)整檢測探頭與焊縫之間的角度與距離。因此,針對于管道相貫線焊縫的自動(dòng)化無損檢測機(jī)器人必須具備自動(dòng)尋跡以及對焊縫缺陷定位的能力�；趯艿老嘭灳�焊縫無損檢測實(shí)際需求及現(xiàn)場應(yīng)用場景的詳細(xì)分析和考量,并對比多種機(jī)器人領(lǐng)域常用的自主尋跡和定位方案之后,本文確立了管道相貫線焊縫檢測機(jī)器人通過自動(dòng)識別磁性色碼條實(shí)現(xiàn)視覺引導(dǎo)并通過多傳感器信息融合實(shí)現(xiàn)準(zhǔn)確定位的方案。針對確立的視覺引導(dǎo)與信息融合定位的方案,本文先提出了一種機(jī)器視覺引導(dǎo)方案下的圖像處理算法。該圖像處理算法在顏色空間轉(zhuǎn)換和彩色邊緣檢測基礎(chǔ)上,引入圖像金字塔方法的Hough變換直線檢測算法,快速識別磁性色碼條并實(shí)現(xiàn)機(jī)器人自身姿態(tài)和運(yùn)動(dòng)軌跡的實(shí)時(shí)控制,將多閾值處理引入?yún)^(qū)域生長算法分割人工定位標(biāo)識點(diǎn),對人工定位標(biāo)識點(diǎn)進(jìn)行計(jì)數(shù)從而輔助機(jī)器人實(shí)現(xiàn)定位。然后本文根據(jù)機(jī)器人沿著管道相貫線焊縫運(yùn)動(dòng)的這一限制性條件,對機(jī)器人的運(yùn)動(dòng)軌跡和自身姿態(tài)進(jìn)行建模,分析和推導(dǎo)機(jī)器人在運(yùn)動(dòng)過程中自身姿態(tài)變化與運(yùn)動(dòng)軌跡之間的數(shù)學(xué)關(guān)系。利用光電編碼器和人工定位標(biāo)識點(diǎn)計(jì)數(shù)建立起里程計(jì)定位模型,同時(shí)利用IMU系統(tǒng)建立起IMU定位模型。最后根據(jù)推導(dǎo)出的數(shù)學(xué)關(guān)系,利用Kalman濾波融合IMU模型和里程計(jì)模型這兩大定位模型的信息,實(shí)現(xiàn)多傳感器信息融合下的機(jī)器人定位功能。通過MATLAB軟件,分別對里程計(jì)模型、IMU模型以及多傳感器信息融合模型進(jìn)行了仿真,分析和對比了三種定位模型的結(jié)果和誤差。實(shí)驗(yàn)表明,本文提出的圖像處理算法能夠使機(jī)器人穩(wěn)定識別出磁性色碼條,從而實(shí)現(xiàn)機(jī)器人的自動(dòng)尋跡功能。同時(shí),圖像處理算法還能準(zhǔn)確的分割出定位標(biāo)識點(diǎn)以輔助機(jī)器人實(shí)現(xiàn)定位。而計(jì)算機(jī)的仿真結(jié)果顯示,基于Kalman濾波下的多傳感器信息融合能夠?qū)崿F(xiàn)機(jī)器人對自身的準(zhǔn)確定位,其定位精度顯著優(yōu)于單獨(dú)使用光電編碼器或者IMU系統(tǒng)。
[Abstract]:In the field of nondestructive testing, the detection of pipeline intersecting line weld defects is difficult and the detection efficiency is low. The pipe intersecting line weld detection robot attached to the steel pipe wall by magnetic wheel needs to carry the nondestructive testing equipment to move along the intersecting line weld and detect the weld defects. In the process of detection, the angle and distance between the detection probe and the weld should be adjusted constantly. Therefore, the automatic nondestructive testing robot for pipeline intersecting line weld must have the ability of automatic tracing and weld defect location. Based on the detailed analysis and consideration of the actual requirements of nondestructive testing of pipeline intersecting line welds and the field application scenarios, and after comparing various autonomous tracing and positioning schemes commonly used in the field of robots, In this paper, a scheme of visual guidance and accurate positioning of pipeline intersecting line weld detection robot through automatic recognition of magnetic color code bar and multi-sensor information fusion is established. Aiming at the established scheme of visual guidance and information fusion positioning, an image processing algorithm based on machine vision guidance scheme is proposed in this paper. On the basis of color space conversion and color edge detection, the image processing algorithm introduces the Hough transform line detection algorithm of image pyramid method to quickly identify the magnetic color code bar and realize the real-time control of the robot's own attitude and motion trajectory. The multi-threshold processing is introduced into the region growth algorithm to segment the artificial positioning identification points, and the artificial positioning identification points are counted to assist the robot to realize the positioning. Then, according to the restrictive condition of the weld motion of the robot along the intersecting line of the pipe, the trajectory and attitude of the robot are modeled in this paper. The mathematical relationship between the attitude change and the trajectory of the robot in the process of motion is analyzed and deduced. The odometer positioning model is established by using photoelectric encoder and manual positioning marking point counting, and the IMU positioning model is established by using IMU system. Finally, according to the derived mathematical relationship, Kalman filter is used to fuse the information of IMU model and odometer model to realize the robot positioning function under multi-sensor information fusion. The odometer model, IMU model and multi-sensor information fusion model are simulated by MATLAB software, and the results and errors of the three positioning models are analyzed and compared. The experimental results show that the image processing algorithm proposed in this paper can make the robot recognize the magnetic color code bar stably, so as to realize the automatic tracing function of the robot. At the same time, the image processing algorithm can also accurately segment the positioning identification points to assist the robot to achieve positioning. The computer simulation results show that the multi-sensor information fusion based on Kalman filter can realize the accurate positioning of the robot, and its positioning accuracy is significantly better than that of photoelectric encoder or IMU system alone.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242

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