诺森Error compensation for industrial robotsWenhe Liao[等著]

Contents

Part I Theories

Chapter 1 Introduction 3

1.1 Background 3

1.2 What is robot accuracy 6

1.3 Why error compensation 8

1.4 Early investigations and insights 9

1.4.1 Offline calibration 10

1.4.2 Online feedback 16

1.5 Summary 19

Chapter 2 Kinematic modeling 21

2.1 Introduction 21

2.2 Pose description and transformation 21

2.2.1 Descriptions of position and posture 21

2.2.2 Translation and rotation 22

. RPY angle and Euler angle

2.4 Forward kinematics 26

2.4.1 Link description and link frame 26

2.4.2 Link transformation and forward kinematic model 27

2.4.3 Forward kinematic model of a typical KUKA industrial robot 29

2.5 Inverse kinematics 33

2.5.1 Uniquely closed solution with joint constraints 34

2.5.2 Inverse kinematic model of a typical KUKA industrial robot 35

2.6 Error modeling 38

2.6.1 Differential transformation 38

2.6.2 Differential transformation of consecutive links 40

2.6.3 Kinematic error model 42

2.7 Summary 44

Chapter 3 Positioning error compensation using kinematic calibration 45

3.1 Introduction 45

3.2 Observability-index-based random sampling method 46

3.2.1 Observability index of robot kinematic parameters 46

3.2.2 Selection method of sampling points 48

3.3 Uniform-grid-based sampling method 54

3.3.1 Optimal grid size 54

3.3.2 Sampling point planning method 67

3.4 Kinematic calibration considering robot flelity error 73

3.4.1 Robot flelity analysis 74

3.4.2 Establishment of robot flelity error model 76

3.4.3 Robot kinematic error model with flelity error 77

3.5 Kinematic calibration using variable parametric error 79

3.6 Parameter identification using L-M algorithm 81

3.7 Verification of error compensation performance 83

3.7.1 Kinematic calibration with robot flelity error 83

3.7.2 Error compensation using variable parametric error 84

3.8 Summary 91

Chapter 4 Error-similarity-based positioning error compensation 92

4.1 Introduction 92

4.2 Similarity of robot positioning error 93

4.2.1 litative analysis of error similarity 93

4.2.2 ntitative analysis of error similarity 94

4.. Numerical simulation and discussion 96

4.3 Error compensation based on inverse distance weighting and error similarity 100

4.3.1 Inverse distance weighting interpolation methd 01

4.3.2 Error compensation method combined W with error similarity 102

4.3.3 Numerical simulation and discussion 104

4.4 Error compensation based on linear unbiased optimal estimation and error similarity 106

4.4.1 Robot positioning error mapping based on error similarity 106

4.4.2 Linear unbiased optimal estimation of robot positioning errr 09

4.4.3 Numerical simulation and discussion 112

4.4.4 Error compensation 116

4.5 Optimal sampling based on error similarity 116

4.5.1 Mathematical model of optimal sampling points 117

4.5.2 Multi-objective optimization and non-inferior solution 119

4.5.3 Genetic algorithm and NSGA-II 121

4.5.4 Multi-objective optimization of optimal sampling points of robots based on NSGA-II 128

4.6 Experimental verification 131

4.6.1 Experimental platform 131

4.6.2 Experimental verification of positioning error similarity 133

4.6.3 Experimental verification of error compensation based on inverse distance weighting and error similarity 141

4.6.4 Experimental verification of error compensation based on linear unbiased optimal estimation and error similarity 145

4.7 Summary 148

Chapter 5 Joinspceclsed-loop feedback 149

5.1 Introduction 149

5.2 Positioning error estimation 149

5.2.1 Error estimation model of Chebyshev polynomial 149

5.2.2 Identification of Chebyshev coefficients 153

5.. Mapping model 154

5.3 Effect of joint backlash on positioning errr 55

5.3.1 Variation law of joint backlash 155

5.3.2 Multi-directional positioning accuracy variation 158

5.4 Error compensation using feedforward and feedback loops 161

5.5 Experimental verification and analysis 162

5.5.1 Experimental setup 162

5.5.2 Error estimation experiment 163

5.5.3 Error compensation experiment 165

5.6 Summary 167

Chapter 6 Cartesian space closed-loop feedback 168

6.1 Introduction 168

6.2 Pose measurement using binocular visual sensr 68

6.2.1 Description of frame 168

6.2.2 Pose measurement principle based on binocular vision 170

6.. Influence of the frame FE on measurement accuracy 174

6.2.4 Pose estimation using Kalman filtering 177

6.3 Vision-guided control system 178

6.4 Experimental verification 183

6.4.1 Experimental platform 183

6.4.2 Kalman-filtering-based estimation 184

6.4.3 No-load experiment 185

6.5 Summary 189

Part II Applications

Chapter 7 Applications in robotic drilling 193

7.1 Introduction 193

7.2 Robotic drilling system 193

7.2.1 Hardware 193

7.2.2 Software 196

7.3 Establishment of frames 198

7.3.1 Worl fae 198

7.3.2 Robot base frame 200

7.3.3 Robot ane frame 201

7.3.4 Tool frame 203

7.3.5 Product frame 204

7.3.6 Transformation of frames 205

7.4 Drilling applications 207

7.4.1 Error-similarity-based error compensation 207

7.4.2 Joinspceclsed-loop feedback 210

7.4.3 Cartesian space closed-loop feedback 212

7.5 Summary 214

Chapter 8 Applications in robotic milling 215

8.1 Introduction 215

8.2 Robotic milling system 215

8.3 Milling on aluminum alloy part 216

8.3.1 Line milling 216

8.3.2 Arc milling 217

8.4 Milling on cylinder head for car engine 219

8.4.1 Line milling 221

8.4.2 Plane milling 222

8.5 Edge milling on coite shell 2

8.6 Summary 225

References 226<

本书详细地介绍了工业机器人精度补偿的基础理论和关键技术，主要内容包括：机器人运动学模型建方和机器人定位误差分析，机器人运动学模型标定方法，机器人非运动学标定方法，机器人很优采样点规划方法等，并进一步阐述了飞机装配自动制孔系统中工业机器人精度补偿技术的应用方法，以验该技术的有效。