Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators

This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century.

An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems.

* Provides a fully connected, systematic, methodological, consecutive, and easy approach to solving redundancy resolution problems

* Describes a new approach to the time-varying Jacobian matrix pseudoinversion, applied to the redundant-manipulator kinematic control

* Introduces The QP-based unification of robots' redundancy resolution

* Illustrates the effectiveness of the methods presented using a large number of computer simulation results based on PUMA560, PA10, and planar robot manipulators

* Provides technical details for all schemes and solvers presented, for readers to adopt and customize them for specific industrial applications

Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.

Yunong Zhang, PhD, is a professor at the School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China, and an associate editor at IEEE Transactions on Neural Networks and Learning Systems. He has researched motion planning and control of redundant manipulators and recurrent neural networks for 19 years, and he holds seven authorized patents.

Long Jin is pursuing his doctorate in Communication and Information Systems at the School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China. His main research interests include robotics, neural networks, and intelligent information processing.

Zusammenfassung

Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators

This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century.

An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems.

• Provides a fully connected, systematic, methodological, consecutive, and easy approach to solving redundancy resolution problems
• Describes a new approach to the time-varying Jacobian matrix pseudoinversion, applied to the redundant-manipulator kinematic control
• Introduces The QP-based unification of robots' redundancy resolution
• Illustrates the effectiveness of the methods presented using a large number of computer simulation results based on PUMA560, PA10, and planar robot manipulators
• Provides technical details for all schemes and solvers presented, for readers to adopt and customize them for specific industrial applications

Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.

List of Figures xiii

List of Tables xxv

Preface xxvii

Acknowledgments xxxiii

Acronyms xxxv

Part I Pseudoinverse-Based ZD Approach 1

1 Redundancy Resolution via Pseudoinverse and ZD Models 3

1.1 Introduction 3

1.2 Problem Formulation and ZD Models 5

1.2.1 Problem Formulation 5

1.2.2 Continuous-Time ZD Model 6

1.2.3 Discrete-Time ZD Models 7

1.2.3.1 Euler-Type DTZD Model with J (t) Known 7

1.2.3.2 Euler-Type DTZD Model with J (t) Unknown 7

1.2.3.3 Taylor-Type DTZD Models 8

1.3 ZD Applications to Different-Type Robot Manipulators 9

1.3.1 Application to a Five-Link Planar Robot Manipulator 9

1.3.2 Application to a Three-Link Planar Robot Manipulator 12

1.4 Chapter Summary 14

Part II Inverse-Free Simple Approach 15

2 G1 Type Scheme to JVL Inverse Kinematics 17

2.1 Introduction 17

2.2 Preliminaries and RelatedWork 18

2.3 Scheme Formulation 18

2.4 Computer Simulations 19

2.4.1 Square-Path Tracking Task 19

2.4.2 Z-Shaped Path Tracking Task 22

2.5 Physical Experiments 25

2.6 Chapter Summary 26

3 D1G1 Type Scheme to JAL Inverse Kinematics 27

3.1 Introduction 27

3.2 Preliminaries and RelatedWork 28

3.3 Scheme Formulation 28

3.4 Computer Simulations 29

3.4.1 Rhombus-Path Tracking Task 29

3.4.1.1 Verifications 29

3.4.1.2 Comparisons 30

3.4.2 Triangle-Path Tracking Task 32

3.5 Chapter Summary 36

4 Z1G1 Type Scheme to JAL Inverse Kinematics 37

4.1 Introduction 37

4.2 Problem Formulation and Z1G1 Type Scheme 37

4.3 Computer Simulations 38

4.3.1 Desired Initial Position 38

4.3.1.1 Isosceles-Trapezoid Path Tracking 40

4.3.1.2 Isosceles-Triangle Path Tracking 41

4.3.1.3 Square Path Tracking 42

4.3.2 Nondesired Initial Position 44