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，机器人最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。

Wenhe Liao,Nanjing University of Aeronautics and Astronautics,Nanjing University of Science and Technology Nanjing, Jiangsu, China.

Part Ⅰ Theories
Chapter 1 Introduction
1.1 Background
1.2 What is robot accuracy
1.3 Why error compensation
1.4 Early investigations and insights
1.4.1 Offline calibration
1.4.2 Online feedback
1.5 Summary
Chapter 2 Kinematic modeling
2.1 Introduction
2.2 Pose description and transformation
2.2.1 Descriptions of position and posture
2.2.2 Translation and rotation
2.3 RPY angle and Euler angle
2.4 Forward kinematics
2.4.1 Link description and link frame
2.4.2 Link transformation and forward kinematic model
2.4.3 Forward kinematic model of a typical KUKA industrial robot
2.5 Inverse kinematics
2.5.1 Uniquely closed solution with joint constraints
2.5.2 Inverse kinematic model of a typical KUKA industrial robot
2.6 Error modeling
2.6.1 Differential transformation
2.6.2 Differential transformation of consecutive links
2.6.3 Kinematic error model
2.7 Summary
Chapter 3 Positioning error compensation using kinematic calibration
3.1 Introduction
3.2 Observability-index-based random sampling method
3.2.1 Observability index of robot kinematic parameters
3.2.2 Selection method of sampling points
3.3 Uniform-grid-based sampling method
3.3.1 Optimal grid size
3.3.2 Sampling point planning method
3.4 Kinematic calibration considering robot flexibility error
3.4.1 Robot flexibility analysis
3.4.2 Establishment of robot flexibility error model
3.4.3 Robot kinematic error model with flexibility error
3.5 Kinematic calibration using variable parametric error
3.6 Parameter identification using L-M algorithm
3.7 Verification of error compensation performance
3.7.1 Kinematic calibration with robot flexibility error
3.7.2 Error compensation using variable parametric error
3.8 Summary
Chapter 4 Error-similarity-based positioning error compensation
4.1 Introduction
4.2 Similarity of robot positioning error
4.2.1 Qualitative analysis of error similarity
4.2.2 Quantitative analysis of error similarity
4.2.3 Numerical simulation and discussion
4.3 Error compensation based on inverse distance weighting and error similarity
4.3.1 Inverse distance weighting interpolation method
4.3.2 Error compensation method combined IDW with error similarity
4.3.3 Numerical simulation and discussion
4.4 Error compensation based on linear unbiased optimal estimation and error similarity
4.4.1 Robot positioning error mapping based on error similarity
4.4.2 Linear unbiased optimal estimation of robot positioning error
4.4.3 Numerical simulation and discussion
4.4.4 Error compensation
4.5 Optimal sampling based on error similarity
4.5.1 Mathematical model of optimal sampling points
4.5.2 Multi-objective optimization and non-inferior solution
4.5.3 Genetic algorithm and NSGA-II
4.5.4 Multi-objective optimization of optimal sampling points of robots based on NSGA-II
4.6 Experimental verification
4.6.1 Experimental platform
4.6.2 Experimental verification of positioning error similarity
4.6.3 Experimental verification of error compensation based on inverse distance weighting and error similarity
4.6.4 Experimental verification of error compensation based on linear unbiased optimal estimation and error similarity
4.7 Summary
Chapter 5 Joint space closed-loop feedback
5.1 Introduction
5.2 Positioning error estimation
5.2.1 Error estimation model of Chebyshev polynomial
5.2.2 Identification of Chebyshev coefficients
5.2.3 Mapping model
5.3 Effect of joint backlash on positioning error
5.3.1 Variation law of joint backlash
5.3.2 Multi-d