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E-grāmata: Robot Manipulator Redundancy Resolution [Wiley Online]

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  • Formāts: 320 pages
  • Sērija : Wiley-ASME Press Series
  • Izdošanas datums: 03-Nov-2017
  • Izdevniecība: Wiley-ASME Press
  • ISBN-10: 1119381444
  • ISBN-13: 9781119381440
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  • Wiley Online
  • Cena: 132,41 €*
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Robot Manipulator Redundancy ResolutionPalielināt
  • Formāts: 320 pages
  • Sērija : Wiley-ASME Press Series
  • Izdošanas datums: 03-Nov-2017
  • Izdevniecība: Wiley-ASME Press
  • ISBN-10: 1119381444
  • ISBN-13: 9781119381440
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119381440

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
7(68)
1 Redundancy Resolution via Pseudoinverse and ZD Models
3(72)
1.1 Introduction
3(2)
1.2 Problem Formulation and ZD Models
5(4)
1.2.1 Problem Formulation
5(1)
1.2.2 Continuous-Time ZD Model
6(1)
1.2.3 Discrete-Time ZD Models
7(1)
1.2.3.1 Euler-Type DTZD Model with (t) Known
7(1)
1.2.3.2 Euler-Type DTZD Model with (t) Unknown
7(1)
1.2.3.3 Taylor-Type DTZD Models
8(1)
1.3 ZD Applications to Different-Type Robot Manipulators
9(65)
1.3.1 Application to a Five-Link Planar Robot Manipulator
9(3)
1.3.2 Application to a Three-Link Planar Robot Manipulator
12(2)
1.4
Chapter Summary
14(1)
Part II Inverse-Free Simple Approach
15(2)
2 G1 Type Scheme to JVL Inverse Kinematics
17(1)
2.1 Introduction
17(1)
2.2 Preliminaries and Related Work
18(1)
2.3 Scheme Formulation
18(1)
2.4 Computer Simulations
19(1)
2.4.1 Square-Path Tracking Task
19(3)
2.4.2 "Z"-Shaped Path Tracking Task
22(3)
2.5 Physical Experiments
25(1)
2.6
Chapter Summary
26(1)
3 D1G1 Type Scheme to JAL Inverse Kinematics
27(10)
3.1 Introduction
27(1)
3.2 Preliminaries and Related Work
28(1)
3.3 Scheme Formulation
28(1)
3.4 Computer Simulations
29(7)
3.4.1 Rhombus-Path Tracking Task
29(1)
3.4.1.1 Verifications
29(1)
3.4.1.2 Comparisons
30(2)
3.4.2 Triangle-Path Tracking Task
32(4)
3.5
Chapter Summary
36(1)
4 Z1G1 Type Scheme to JAL Inverse Kinematics
37(10)
4.1 Introduction
37(1)
4.2 Problem Formulation and Z1G1 Type Scheme
37(1)
4.3 Computer Simulations
38(7)
4.3.1 Desired Initial Position
38(2)
4.3.1.1 Isosceles-Trapezoid Path Tracking
40(1)
4.3.1.2 Isosceles-Triangle Path Tracking
41(1)
4.3.1.3 Square Path Tracking
42(2)
4.3.2 Nondesired Initial Position
44(1)
4.4 Physical Experiments
45(1)
4.5
Chapter Summary
45(2)
Part III QP Approach and Unification
47(20)
5 Redundancy Resolution via QP Approach and Unification
49(18)
5.1 Introduction
49(1)
5.2 Robotic Formulation
50(2)
5.3 Handling Joint Physical Limits
52(1)
5.3.1 Joint-Velocity Level
52(1)
5.3.2 Joint-Acceleration Level
52(1)
5.4 Avoiding, Obstacles
53(1)
5.5 Various Performance Indices
54(2)
5.5.1 Resolved at Joint-Velocity Level
55(1)
5.5.1.1 MVN scheme
55(1)
5.5.1.2 RMP scheme
55(1)
5.5.1.3 MKE scheme
55(1)
5.5.2 Resolved at Joint-Acceleration Level
55(1)
5.5.2.1 MAN scheme
55(1)
5.5.2.2 MTN scheme
56(1)
5.5.2.3 IIWT scheme
56(1)
5.6 Unified QP Formulation
56(1)
5.7 Online QP Solutions
57(4)
5.7.1 Traditional QP Routines
57(1)
5.7.2 Compact QP Method
57(1)
5.7.3 Dual Neural Network
57(1)
5.7.4 LVI-Aided Primal-Dual Neural Network
57(2)
5.7.5 Numerical Algorithms E47 and 94LVI
59(1)
5.7.5.1 Numerical Algorithm E47
59(1)
5.7.5.2 Numerical Algorithm 94LVI
59(2)
5.8 Computer Simulations
61(5)
5.9
Chapter Summary
66(1)
Part IV Illustrative JVLQP Schemes and Performances
67(70)
6 Varying Joint-Velocity Limits Handled by QP
69(26)
6.1 Introduction
69(1)
6.2 Preliminaries and Problem Formulation
70(6)
6.2.1 Six-DOF Planar Robot System
70(3)
6.2.2 Varying Joint-Velocity Limits
73(3)
6.3 94LVI Assisted QP Solution
76(1)
6.4 Computer Simulations and Physical Experiments
77(15)
6.4.1 Line-Segment Path-Tracking Task
77(8)
6.4.2 Elliptical-Path Tracking Task
85(2)
6.4.3 Simulations with Faster Tasks
87(1)
6.4.3.1 Line-Segment-Path-Tracking Task
87(2)
6.4.3.2 Elliptical-Path-Tracking Task
89(3)
6.5
Chapter Summary
92(3)
7 Feedback-Aided Minimum Joint Motion
95(26)
7.1 Introduction
95(2)
7.2 Preliminaries and Problem Formulation
97(4)
7.2.1 Minimum Joint Motion Performance Index
97(3)
7.2.2 Varying Joint-Velocity Limits
100(1)
7.3 Computer Simulations and Physical Experiments
101(18)
7.3.1 "M"-Shaped Path-Tracking Task
101(1)
7.3.1.1 Simulation Comparisons with Different Kp
101(2)
7.3.1.2 Simulation Comparisons with Different γ
103(2)
7.3.1.3 Simulative and Experimental Verifications of FAMJM Scheme
105(2)
7.3.2 "P"-Shaped Path Tracking Task
107(1)
7.3.3 Comparisons with Pseudoinverse-Based Approach
108(2)
7.3.3.1 Comparison with Tracking Task of Larger "M"-Shaped Path
110(2)
7.3.3.2 Comparison with Tracking Task of Larger "P"-Shaped Path
112(7)
7.4
Chapter Summary
119(2)
8 QP Based Manipulator State Adjustment
121(16)
8.1 Introduction
121(1)
8.2 Preliminaries and Scheme Formulation
122(2)
8.3 QP Solution and Control of Robot Manipulator
124(1)
8.4 Computer Simulations and Comparisons
125(7)
8.4.1 State Adjustment without ZIV Constraint
125(3)
8.4.2 State Adjustment with ZIV Constraint
128(4)
8.5 Physical Experiments
132(4)
8.6
Chapter Summary
136(1)
Part V Self-Motion Planning
137(62)
9 QP-Based Self-Motion Planning
139(22)
9.1 Introduction
139(1)
9.2 Preliminaries and QP Formulation
140(1)
9.2.1 Self-Motion Criterion
140(1)
9.2.2 QP Formulation
141(1)
9.3 LVIAPDNN Assisted QP Solution
141(1)
9.4 PUMA560 Based Computer Simulations
142(10)
9.4.1 From Initial Configuration A to Desired Configuration B
144(2)
9.4.2 From Initial Configuration A to Desired Configuration C
146(1)
9.4.3 From Initial Configuration E to Desired Configuration F
147(5)
9.5 PA10 Based Computer Simulations
152(6)
9.6
Chapter Summary
158(3)
10 Pseudoinverse Method and Singularities Discussed
161(22)
10.1 Introduction
161(1)
10.2 Preliminaries and Scheme Formulation
162(2)
10.2.1 Modified Performance Index for SMP
163(1)
10.2.2 QP-Based SMP Scheme Formulation
163(1)
10.3 LVIAPDNN Assisted QP Solution with Discussion
164(3)
10.4 Computer Simulations
167(13)
10.4.1 Three-Link Redundant Planar Manipulator
168(1)
10.4.1.1 Verifications
168(3)
10.4.1.2 Comparisons
171(1)
10.4.2 PUMA560 Robot Manipulator
172(4)
10.4.3 PA10 Robot Manipulator
176(4)
10.5
Chapter Summary
180(3)
Appendix
181(1)
Equivalence Analysis in Limit Situation
181(2)
11 Self-Motion Planning with ZIV Constraint
183(16)
11.1 Introduction
183(1)
11.2 Preliminaries and Scheme Formulation
184(4)
11.2.1 Handling Joint Physical Limits
184(3)
11.2.2 QP Reformulation
187(1)
11.2.3 Design of ZIV Constraint
187(1)
11.3 E47 Assisted QP Solution
188(1)
11.4 Computer Simulations and Physical Experiments
189(8)
11.5
Chapter Summary
197(2)
Part VI Manipulability Maximization
199(28)
12 Manipulability-Maximizing SMP Scheme
201(10)
12.1 Introduction
201(1)
12.2 Scheme Formulation
202(2)
12.2.1 Derivation of Manipulability Index
202(1)
12.2.2 Handling Physical Limits
203(1)
12.2.3 QP Formulation
203(1)
12.3 Computer Simulations and Physical Experiments
204(5)
12.3.1 Computer Simulations
204(1)
12.3.2 Physical Experiments
205(4)
12.4
Chapter Summary
209(2)
13 Time-Varying Coefficient Aided MM Scheme
211(16)
13.1 Introduction
211(1)
13.2 Manipulability-Maximization with Time-Varying Coefficient
212(4)
13.2.1 Nonzero Initial/Final Joint-Velocity Problem
212(1)
13.2.2 Scheme Formulation
213(2)
13.2.3 94LVI Assisted QP Solution
215(1)
13.3 Computer Simulations and Physical Experiments
216(10)
13.3.1 Computer Simulations
216(8)
13.3.2 Physical Experiments
224(2)
13.4
Chapter Summary
226(1)
Part VII Encoder Feedback and Joystick Control
227(34)
14 QP Based Encoder Feedback Control
229(22)
14.1 Introduction
229(2)
14.2 Preliminaries and Scheme Formulation
231(3)
14.2.1 Joint Description
231(1)
14.2.2 OMPFC Scheme
231(3)
14.3 Computer Simulations
234(6)
14.3.1 Petal-Shaped Path-Tracking Task
234(4)
14.3.2 Comparative Simulations
238(1)
14.3.2.1 Petal-Shaped Path Tracking Using Another Group of Joint-Angle Limits
238(1)
14.3.2.2 Petal-Shaped Path Tracking via the Method 4 (M4) Algorithm
238(1)
14.3.3 Hexagonal-Path-Tracking Task
239(1)
14.4 Physical Experiments
240(8)
14.5
Chapter Summary
248(3)
15 QP Based Joystick Control
251(10)
15.1 Introduction
251(1)
15.2 Preliminaries and Hardware System
251(2)
15.2.1 Velocity-Specified Inverse Kinematics Problem
252(1)
15.2.2 Joystick-Controlled Manipulator Hardware System
252(1)
15.3 Scheme Formulation
253(1)
15.3.1 Cosine-Aided Position-to-Velocity Mapping
253(1)
15.3.2 Real-Time Joystick-Controlled Motion Planning
254(1)
15.4 Computer Simulations and Physical Experiments
254(5)
15.4.1 Movement Toward Four Directions
255(4)
15.4.2 "MVN" Letter Writing
259(1)
15.5
Chapter Summary
259(2)
References 261(16)
Index 277
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.
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