工业机器人智能平面及空间视觉伺服控制算法研究

发布时间：2023-01-12 17:36

　　多年来,机器人感知是一个重要的研究领域,其中计算机视觉在机器人的环境感知方面发挥着重要作用。基于视觉反馈的机器人控制称为视觉伺服控制。为了提高视觉伺服控制的技术水平,使其能够在工业场景中稳定地应用,有关学者进行了各种各样的尝试。然而至今为止,在实际复杂的工业环境中对视觉伺服进行深入研究的案例很少。本文基于大量实验,介绍了计算机视觉和视觉伺服算法在需求广泛的工业场景中的应用。首先,本文研究了基于位置的视觉伺服的汽车零件喷涂机器人的设计方法。本文采用了点云处理的方法,使其能够实时感知各种类型的几何图形。结合降维的方法,将三维点云投影到二维平面子空间上,并运用轮廓搜索等二维图像处理的方法。本文提出了一种改进的特征提取方法,该方法使用扇形模型从汽车零件的二维图像中提取有用的特征。本文采用统计匹配的方法从有限集合中识别出汽车零件,并与目前点云处理中广泛使用的目标识别方法进行计算成本比较。比较结果表明,相比其他目标识别方法中的特征提取方法,该算法具有更好的实时性。在姿态估计部分,本文采用了著名的迭代最近点算法,将其与遗传算法相结合,解决了其收敛于局部最优的问题。与传统的迭代最近点算法相比,改进后的... 

【文章页数】：183 页

【学位级别】：博士

【文章目录】：
摘要
Abstract
Chapter 1 Introduction to Visual Servoing
    1.1 Computer Vision in Industry
    1.2 Notation
    1.3 Problem Formulation
        1.3.1 Camera Modeling
        1.3.2 Visual Servo Modeling
    1.4 Image based Visual Servo
    1.5 Position based Visual Servo
    1.6 Direct Visual Servo
    1.7 Research Targets and Contributions
    1.8 Organization of the Manuscript
        1.8.1 Part I: Improving the Traditional state of the art for Industrial Applications
        1.8.2 Part II: Advancing Current state of the art with Developments in Artifi-cial Intelligence
    1.9 Bibliographical Remarks
Chapter 2 Recognition and Pose Estimation of Auto Parts for an AutonomousSpray Painting Robot
    2.1 Introduction
        2.1.1 Object Recognition
        2.1.2 Pose Estimation
        2.1.3 Contribution Summary
    2.2 Overview of Car Part Recognition
        2.2.1 Segmentation of Point Cloud
        2.2.2 Principal Component Analysis
        2.2.3 Contour Searching
        2.2.4 Cross Correlation
    2.3 Feature Selection
    2.4 Object Recognition Pipeline
    2.5 Point Set Registration Pipeline
    2.6 Experimental Results
        2.6.1 Time Complexity Analysis
        2.6.2 Comparative Analysis for Pose Estimation
    2.7 Conclusion
Chapter 3 Quality Inspection of Remote Radio Unit (RRU) power port usingIBVS
    3.1 Introduction
    3.2 Image Features
        3.2.1 Center Points
        3.2.2 Area
        3.2.3 Angle
    3.3 IBVS Control Design
    3.4 Results
        3.4.1 Simulation
        3.4.2 Experiment
    3.5 Conclusion
Chapter 4 Quality Inspection of Remote Radio Units using Depth-free Imagebased Visual Servo with Acceleration Command
    4.1 Introduction
        4.1.1 Detection of Power Port
        4.1.2 IBVS Control Design
        4.1.3 Contribution Summary
    4.2 Computer Vision Pipeline
        4.2.1 Extraction of Region of Interest
        4.2.2 Region of Interest Tracking using Camshift
        4.2.3 Improved Camshift Tracking
    4.3 Active Auto-Focus
    4.4 Features Selection
    4.5 Derivation of Depth Free Image Jacobian
    4.6 IBVS Control Design
        4.6.1 Problem Formulation
        4.6.2 Acceleration Command
        4.6.3 Stability Analysis
    4.7 Results
        4.7.1 Experimental Validation of Computer Vision Pipeline
        4.7.2 Simulation Results
        4.7.3 Experimental Validation
    4.8 Conclusion
Chapter 5 Position based Visual Servoing in Joint Space with Deep NeuralNetworks
    5.1 Introduction
        5.1.1 Monocular Pose Estimation
        5.1.2 PBVS in Joint Space
        5.1.3 Contribution Summary
    5.2 Position based Visual Servoing using Deep CNN
        5.2.1 Theoretical Preliminaries
        5.2.2 Deep CNN in Joint Space
    5.3 Preparation of Dataset
        5.3.1 Homography and Virtual Camera Setup
        5.3.2 Incorporating Robot Kinematics
        5.3.3 Brightness and Contrast Variation
        5.3.4 Introduction of Occlusions
    5.4 Control Design
    5.5 Experimental Validation
        5.5.1 Training the Networks
        5.5.2 PBVS Control
    5.6 Conclusion
Chapter 6 Deep Siamese Convolutional Neural Networks for Scene Identification
    6.1 Introduction
    6.2 Related Work
    6.3 Preliminaries
        6.3.1 Reinforcement Learning in Visual Servo
        6.3.2 Sparse vs Dense Rewards Modeling
        6.3.3 Siamese Networks for Scene Detection
    6.4 Preparation of Data set
        6.4.1 Affine Transformation of Images
        6.4.2 Brightness Variation
        6.4.3 Occlusion Handling
    6.5 Network Architecture
    6.6 Results
        6.6.1 Generation of Data set
        6.6.2 Network Training
        6.6.3 Network Performance Analysis
        6.6.4 Comparative Analysis with Naive Keypoints Matching
    6.7 Conclusion
结论
Conclusions
References
攻读博士学位期间发表的论文及其他成果
致谢
个人简历



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