Robot Manipulator Redundancy Resolution

Yunong Zhang , Long Jin 지음

Wiley 2017년 09월 11일 출간

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책소개

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 manipula

목차

List of Figures xi List of Tables xxvii Preface xxix Acknowledgments xxxv Acronyms xxxvii PART I PSEUDOINVERSEBASED ZD APPROACH 1 Redundancy Resolution via Pseudoinverse and ZD Models 3 1.1 Introduction 4 1.2 Problem Formulation and ZD Models 6 1.3 ZD Applications to DifferentType Robot Manipulators 11 1.4 Chapter Summary 16 PART II INVERSE FREE SIMPLE APPROACH 2 G1 Type Scheme to JVL Inverse Kinematics 19 2.1 Introduction 20 2.2 Preliminaries and Related Work 21 2.3 Scheme Formulation 24 2.4 Computer Simulations 27 2.5 Physical Experiments 28 2.6 Chapter Summary 28 3 D1G1 Type Scheme to JAL Inverse Kinematics 33 3.1 Introduction 34 3.2 Preliminaries and Related Work 34 3.3 Scheme Formulation 37 3.4 Computer Simulations 40 3.5 Chapter Summary 44 4 Z1G1 Type Scheme to JAL Inverse Kinematics 45 4.1 Introduction 46 4.2 Problem Formulation and Z1G1 Type Scheme 46 4.3 Computer Simulations 47 4.4 Physical Experiments 52 4.5 Chapter Summary 55 PART III QP APPROACH AND UNIFICATION 5 Redundancy Resolution via QP Approach and Unification 59 5.1 Introduction 60 5.2 Robotic Formulation 61 5.3 Handling Joint Physical Limits 63 5.4 Avoiding Obstacles 64 5.5 Various Performance Indices 66 5.6 Unified QP Formulation 67 5.7 Online QP Solutions 68 5.8 Computer Simulations 73 5.9 Chapter Summary 78 PART IV ILLUSTRATIVE JVL QP SCHEMES AND PERFORMANCES 6 Varying Joint Velocity Limits Handled by QP 83 6.1 Introduction 84 6.2 Preliminaries and Problem Formulation 84 6.3 94LVI Assisted QP Solution 92 6.4 Computer Simulations and Physical Experiments 93 6.5 Chapter Summary 110 7 FeedbackAided Minimum Joint Motion 111

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저자(글) Yunong Zhang

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.

저자(글) Long Jin