Robot Manipulator Redundancy Resolution

Name: Robot Manipulator Redundancy Resolution
Brand: Wiley
Price: 101.99 EUR
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Yunong Zhang Long Jin(Autor*in)

Wiley (Verlag)

Erschienen am 6. September 2017

320 Seiten

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Beschreibung

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.

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Inhalt

Intro
Title Page
Copyright
Dedication
Table of Contents
List of Figures
List of Tables
Preface
Acknowledgments
Acronyms
Part I: Pseudoinverse-Based ZD Approach
Chapter 1: Redundancy Resolution via Pseudoinverse and ZD Models
1.1 Introduction
1.2 Problem Formulation and ZD Models
1.3 ZD Applications to Different-Type Robot Manipulators
1.4 Chapter Summary
Part II: Inverse-Free Simple Approach
Chapter 2: G1 Type Scheme to JVL Inverse Kinematics
2.1 Introduction
2.2 Preliminaries and Related Work
2.3 Scheme Formulation
2.4 Computer Simulations
2.5 Physical Experiments
2.6 Chapter Summary
Chapter 3: D1G1 Type Scheme to JAL Inverse Kinematics
3.1 Introduction
3.2 Preliminaries and Related Work
3.3 Scheme Formulation
3.4 Computer Simulations
3.5 Chapter Summary
Chapter 4: Z1G1 Type Scheme to JAL Inverse Kinematics
4.1 Introduction
4.2 Problem Formulation and Z1G1 Type Scheme
4.3 Computer Simulations
4.4 Physical Experiments
4.5 Chapter Summary
Part III: QP Approach and Unification
Chapter 5: Redundancy Resolution via QP Approach and Unification
5.1 Introduction
5.2 Robotic Formulation
5.3 Handling Joint Physical Limits
5.4 Avoiding Obstacles
5.5 Various Performance Indices
5.6 Unified QP Formulation
5.7 Online QP Solutions
5.8 Computer Simulations
5.9 Chapter Summary
Part IV: Illustrative JVL QP Schemes and Performances
Chapter 6: Varying Joint-Velocity Limits Handled by QP
6.1 Introduction
6.2 Preliminaries and Problem Formulation
6.3 94LVI Assisted QP Solution
6.4 Computer Simulations and Physical Experiments
6.5 Chapter Summary
Chapter 7: Feedback-Aided Minimum Joint Motion
7.1 Introduction
7.2 Preliminaries and Problem Formulation
7.3 Computer Simulations and Physical Experiments
7.4 Chapter Summary
Chapter 8: QP Based Manipulator State Adjustment
8.1 Introduction
8.2 Preliminaries and Scheme Formulation
8.3 QP Solution and Control of Robot Manipulator
8.4 Computer Simulations and Comparisons
8.5 Physical Experiments
8.6 Chapter Summary
Part V: Self-Motion Planning
Chapter 9: QP-Based Self-Motion Planning
9.1 Introduction
9.2 Preliminaries and QP Formulation
9.3 LVIAPDNN Assisted QP Solution
9.4 PUMA560 Based Computer Simulations
9.5 PA10 Based Computer Simulations
9.6 Chapter Summary
Chapter 10: Pseudoinverse Method and Singularities Discussed
10.1 Introduction
10.2 Preliminaries and Scheme Formulation
10.3 LVIAPDNN Assisted QP Solution with Discussion
10.4 Computer Simulations
10.5 Chapter Summary
Appendix
Chapter 11: Self-Motion Planning with ZIV Constraint
11.1 Introduction
11.2 Preliminaries and Scheme Formulation
11.3 E47 Assisted QP Solution
11.4 Computer Simulations and Physical Experiments
11.5 Chapter Summary
Part VI: Manipulability Maximization
Chapter 12: Manipulability-Maximizing SMP Scheme
12.1 Introduction
12.2 Scheme Formulation
12.3 Computer Simulations and Physical Experiments
12.4 Chapter Summary
Chapter 13: Time-Varying Coefficient Aided MM Scheme
13.1 Introduction
13.2 Manipulability-Maximization with Time-Varying Coefficient
13.3 Computer Simulations and Physical Experiments
13.4 Chapter Summary
Part VII: Encoder Feedback and Joystick Control
Chapter 14: QP Based Encoder Feedback Control
14.1 Introduction
14.2 Preliminaries and Scheme Formulation
14.3 Computer Simulations
14.4 Physical Experiments
14.5 Chapter Summary
Chapter 15: QP Based Joystick Control
15.1 Introduction
15.2 Preliminaries and Hardware System
15.3 Scheme Formulation
15.4 Computer Simulations and Physical Experiments
15.5 Chapter Summary
References
Index
End User License Agreement

List of Figures

Figure 1.1 Block diagram of a kinematic-control system for a redundant robot manipulator by combining the MVN scheme (1.1) and ZD model, where .
Figure 1.2 Geometry of a five-link planar robot manipulator used in simulations.
Figure 1.3 Joint-angle and joint-velocity profiles of a five-link planar robot manipulator synthesized by pseudoinverse-based MVN scheme (1.1) aided with TDTZD-U model (1.14).
Figure 1.4 (a) Motion process and (b) position error of a five-link planar robot manipulator synthesized by the pseudoinverse-based MVN scheme (1.1) and aided by the TDTZD-U model (1.14).
Figure 1.5 Position error of a five-link planar robot manipulator synthesized by a pseudoinverse-based MVN scheme (1.1) aided with the EDTZD-K model (1.9) or Newton iteration (1.15).
Figure 1.6 Position errors of a three-link planar robot manipulator synthesized by a pseudoinverse-based MVN scheme (1.1) aided with EDTZD-K model (1.9) with .
Figure 2.1 (a) Motion process and (b) joint-angle profiles of a five-link redundant robot manipulator tracking the desired square path synthesized by the G1 type scheme (2.8).
Figure 2.2 (a) Desired path, actual trajectory, and (b) position error of a five-link redundant robot manipulator tracking square path synthesized by a G1 type scheme (2.8).
Figure 2.3 (a) Desired velocity and (b) velocity error of a five-link redundant robot manipulator tracking a square path synthesized by a G1 type scheme (2.8).
Figure 2.4 Motion process (a) and joint-angle profiles (b) of six-DOF redundant robot manipulator tracking the desired "Z"-shaped path synthesized by a G1 type scheme (2.8).
Figure 2.5 (a) Desired path, actual trajectory, and (b) position error of a six-DOF redundant robot manipulator tracking a "Z"-shaped path synthesized by a G1 type scheme (2.8).
Figure 2.6 (a) Desired velocity and (b) velocity error of a six-DOF redundant robot manipulator tracking a "Z"-shaped path synthesized by a G1 type scheme (2.8).
Figure 2.7 Hardware system (a) of six-DOF planar redundant robot manipulator with its structure platform (b). Source: Zhang et al. 2015. Reproduced from Y. Zhang, L. He, J. Ma et al., Inverse-free scheme of G1 type to velocity-level inverse kinematics of redundant robot manipulators, Figure 3, Proceedings of the Twelfth International Symposium on Neural Networks, pp. 99-108, 2015. ©Springer-Verlag 2015. With kind permission of Springer-Verlag (License Number 3978560065761).
Figure 2.8 "Z"-shaped-path-tracking experiment of six-DOF redundant robot manipulator synthesized by G1 type scheme (2.8) at joint-velocity level. Reproduced from Y. Zhang, L. He, J. Ma et al, Inverse-free scheme of G1 type to velocity-level inverse kinematics of redundant robot manipulators, Figure 4, Proceedings of the Twelfth International Symposium on Neural Networks, pp. 99-108, 2015. ©Springer-Verlag 2015. With kind permission of Springer-Verlag (License Number 3978560065761).
Figure 3.1 (a) Motion process, (b) desired path, and actual trajectory of a three-link redundant robot manipulator tracking a rhombus path synthesized by the D1G1 scheme (3.7) with .
Figure 3.2 Joint-angle profiles (a) and position error (b) of a three-link redundant robot manipulator tracking a rhombus path synthesized by the D1G1 scheme (3.7) with .
Figure 3.3 Joint-velocity profiles (a) and velocity error (b) of a three-link redundant robot manipulator tracking a rhombus path synthesized by the D1G1 scheme (3.7) with .
Figure 3.4 (a) Joint-acceleration profiles and (b) acceleration error of a three-link redundant robot manipulator tracking a rhombus path synthesized by the D1G1 scheme (3.7) with .
Figure 3.5 Position errors in rhombus-path-tracking task of a three-link robot manipulator synthesized by (a) pseudoinverse solution (3.4) and (b) inverse-free D1G1 scheme (3.7) with .
Figure 3.6 (a) Motion process and (b) joint-angle profiles of a three-link redundant robot manipulator tracking a desired triangle path synthesized by a D1G1 scheme (3.7) with .
Figure 3.7 Position errors in triangle-path-tracking task of the three-link robot manipulator synthesized by (a) pseudoinverse solution (3.4) and (b) inverse-free D1G1 scheme (3.7) with .
Figure 4.1 (a) Motion process and (b) joint-angle profiles of a three-link planar robot manipulator tracking a desired isosceles-trapezoid path synthesized by the Z1G1 type scheme (4.5).
Figure 4.2 (a) Joint-velocity profiles and (b) joint-acceleration profiles of three-link planar robot manipulator tracking isosceles-trapezoid path synthesized by Z1G1 type scheme (4.5).
Figure 4.3 (a) Motion process and (b) joint-angle profiles of a four-link planar robot manipulator tracking a desired isosceles-triangle path synthesized by the Z1G1 type scheme (4.5).
Figure 4.4 (a) Joint-velocity profiles and (b) joint-acceleration profiles of a four-link planar robot manipulator tracking an isosceles-triangle path synthesized by the Z1G1 type scheme (4.5).
Figure 4.5 Simulation results of a three-link planar robot manipulator tracking an isosceles-trapezoid path synthesized by the Z1G1 type scheme (4.5) with an initial end-effector position not on the desired path.
Figure 4.6 "V"-shaped-path-tracking experiment of a six-DOF redundant robot manipulator synthesized by the Z1G1 type scheme (4.5) at joint-acceleration level.
Figure 5.1 Contradicting situations in equality-based collision-free formulation.
Figure 5.2 QP-based approach to redundancy resolution and torque control.
Figure 5.3 PUMA560 transients synthesized by a QP-based MTN scheme.
Figure 5.4 PUMA560 transients synthesized by a QP-based MKE scheme.
Figure 5.5 Comparison of (a) conventional and (b) presented MTN schemes.
Figure 5.6 Joint-torque profiles synthesized by other QP-based resolution schemes of (a) IIWT and (b) MAN.
Figure 5.7 Joint-torque profiles synthesized by other QP-based resolution schemes of (a ) MKE and (b) MVN.
Figure 6.1 (a) Hardware system and (b) model of a six-DOF planar robot manipulator.
Figure 6.2 Local configuration of a six-DOF planar robot manipulator.
Figure 6.3 Relationship between (a) and as well as (b) the relationship between and .
Figure 6.4 Relationship between (a) and as well as (b) the relationship between and .
Figure 6.5 Relationship between (a) and as well as (b) the relationship between and .
Figure 6.6 Desired line-segment path to be tracked by the end-effector of a six-DOF planar robot manipulator.
Figure 6.7 Snapshots for an actual task execution of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when a robot end-effector tracks a line-segment path. Reproduced from Z. Zhang, and Y. Zhang, Variable Joint-Velocity Limits of Redundant Robot Manipulators Handled by Quadratic Programming, Figure 5, IEEE/ASME Trans. Mechatronics, Vol. 18, No. 2, pp. 674-686, 2013.
Figure 6.8 Actual end-effector trajectory generated by a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme.
Figure 6.9 Simulated six-DOF planar robot manipulator and its joints' trajectories synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks a line-segment path.
Figure 6.10 (a) PPS and (b) PPS transmitted to joint motors when the end-effector tracks a line-segment path.
Figure 6.11 (a) PPS and (b) PPS transmitted to joint motors when the end-effector tracks a line-segment path.
Figure 6.12 (a) PPS and (b) PPS transmitted to joint motors when the end-effector tracks a line-segment path.
Figure 6.13 (a) Joint-angle and (b) joint-velocity profiles of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks a line-segment path.
Figure 6.14 End-effector position error of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks a line-segment path.
Figure 6.15 Snapshots for actual task execution of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks an elliptical path.
Figure 6.16 (a) Joint-angle and (b) joint-velocity profiles of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks an elliptical path.
Figure 6.17 End-effector position error of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when the robot end-effector tracks an elliptical path.
Figure 6.18 Joint-velocity profiles of (a) and (b) of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when its end-effector tracks a line-segment path much faster.
Figure 6.19 Joint-velocity profiles of (a) and (b) of a six-DOF planar robot manipulator synthesized by a VJVL-constrained MVN scheme when its end-effector tracks a line-segment path much...

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