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机器人精度补偿技术与应用(英文版) 版权信息
- ISBN：9787030740182
- 条形码：9787030740182 ; 978-7-03-074018-2
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：
机器人精度补偿技术与应用(英文版) 本书特色
面向飞机装配自动钻铆系统，探索可行可靠的机器人定位误差补偿方法，提升工业机器人绝对定位精度。
机器人精度补偿技术与应用(英文版) 内容简介
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立方法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，以验证该技术的有效性。
机器人精度补偿技术与应用(英文版) 目录
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
2.3 RPY angle and Euler angle 23
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 flexibility error 73
3.4.1 Robot flexibility analysis 74
3.4.2 Establishment of robot flexibility error model 76
3.4.3 Robot kinematic error model with flexibility 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 flexibility 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 Qualitative analysis of error similarity 93
4.2.2 Quantitative analysis of error similarity 94
4.2.3 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 method 101
4.3.2 Error compensation method combined IDW 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 error 109
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 Joint space closed-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
5.2.3 Mapping model 154
5.3 Effect of joint backlash on positioning error 155
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 sensor 168
6.2.1 Description of frame 168
6.2.2 Pose measurement principle based on binocular vision 170
6.2.3 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 Introducti
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
2.3 RPY angle and Euler angle 23
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 flexibility error 73
3.4.1 Robot flexibility analysis 74
3.4.2 Establishment of robot flexibility error model 76
3.4.3 Robot kinematic error model with flexibility 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 flexibility 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 Qualitative analysis of error similarity 93
4.2.2 Quantitative analysis of error similarity 94
4.2.3 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 method 101
4.3.2 Error compensation method combined IDW 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 error 109
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 Joint space closed-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
5.2.3 Mapping model 154
5.3 Effect of joint backlash on positioning error 155
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 sensor 168
6.2.1 Description of frame 168
6.2.2 Pose measurement principle based on binocular vision 170
6.2.3 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 Introducti
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机器人精度补偿技术与应用(英文版) 节选
Part I Theories Chapter 1 Introduction 1.1 Background Currently， as production resources are desired to be capable of rapidly reacting to variations in the market environment， and showing flexibility and efficiency， the requirements for high-precision and flexible manufacturing equipment have been continually increasing in various industrial plants. Industrial robots， which incorporate multiple technologies such as computer science， mechanical engineering， electronic engineering， artificial intelligence， information sensing technology， and control theory， are the product of multi-disciplinary intersections. With the maturity of industrial robot technology， it has become a standard equipment widely used in the industrial automation industry， and its technological development level has also become an important symbol of a country’s level of industrial automation. The deep integration of robot technology and modern manufacturing technology will bring new vitality to existing products and technologies， enhance the comprehensive competitiveness of enterprises， and alleviate the crisis of labor shortage. Recently， due to their high degree of automation， flexibility and adaptability， industrial robots have been widely used in many traditional machining and manufacturing fields. As an example， in the electronic and automotive industries， robots have become a necessary tool for production owing to the variety and quantity of products. There are three reasons why robots are widely used in industrial countries: the first is to reduce labor production costs; the second is to increase labor productivity; the third and most important is to meet the needs of industrialization transformation. With the improvement of the technical level of industrial robots， they have begun to enter the high-precision manufacturing fields such as aerospace manufacturing， microprocessing， and biomedicine. Since the 1990s， the main robot production countries have already developed a robot flexible integrated system for a certain industrial field. Taking industrial robots as the main body， with peripheral manufacturing equipment and related software， forming a robot integrated system that meets a certain hightech manufacturing industry， such as robotic drilling and riveting， robotic welding and robotic fiber placement， etc.， will definitely become the development direction of the manufacturing industry and the robot industry. As the leading industry in the manufacturing fields， aviation manufacturing has always been a strategic industry for the national economy and national defense construction. In recent years， the aircraft manufacturing industry has put forward the requirements of high quality， high efficiency， low cost and adaptation to small-batch and multi-model products for aircraft assembly technology. Aircraft assembly is a process in which aircraft parts or components are combined and connected to form higher-level assemblies or complete aircraft according to the design requirements. It is an extremely important link in the aircraft manufacturing process. So far， aircraft assembly technology has experienced a development process from manual assembly， semi-automatic assembly， automated assembly to flexible assembly. In the assembly process of the aircraft， due to the large size， the complex shape， and the large number of parts and connections of the product， the workload accounts for about 40% to 50% of the total workload. Improving the quality and efficiency of aircraft assembly has become one of the research focuses of today’s aviation manufacturing industry. At present， in the aviation manufacturing industry， drilling and riveting are still dominated by manual operations， which have low work efficiencies as well as unstable assembly qualities. Especially for advanced aircrafts， manual operations have been unable to meet the requirements of technical indicators such as positioning accuracy and normal accuracy of connecting holes. The use of automatic drilling and riveting technology has become an inevitable choice for aircraft assembly today， where the automatic drilling and riveting system based on industrial robots is a current research hot spot. As a kind of automatic equipment integrating advanced technology， industrial robots are very suitable for use in aircraft automatic assembly， e.g.， drilling， riveting， milling， grinding， and fiber placement， as shown in Fig.1.1. Compared with the large and ex-pensive automatic drilling and riveting machines based on CNC machine tools， industrial robots have the advantages of high flexibility， high efficiency， and low manufacturing and maintenance costs. Some giants in the aerospace field have also already developed many robotic aircraft assembly systems， e.g.， the Boeing 777 airframe assembly line by KUKA and Boeing (Fig.1.2)， the early RACE (robot assembly cell) robotic automatic drilling and riveting system and the POWER RACE system by BROETJE Inc
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