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Robot Modeling and Control 2nd edition [Kõva köide]

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(University of Waterloo), (University of Illinois at Urbana-Champaign), (University of Illinois at Urbana-Champaign)
  • Formaat: Hardback, 608 pages, kõrgus x laius x paksus: 246x173x38 mm, kaal: 1225 g
  • Ilmumisaeg: 27-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119523990
  • ISBN-13: 9781119523994
Teised raamatud teemal:
Robot Modeling and Control 2nd editionSuurem pilt
  • Formaat: Hardback, 608 pages, kõrgus x laius x paksus: 246x173x38 mm, kaal: 1225 g
  • Ilmumisaeg: 27-Feb-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119523990
  • ISBN-13: 9781119523994
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119523994.html
Märksõnad:
"A New Edition Featuring Case Studies and Examples of the Fundamentals of Robot Kinematics, Dynamics, and Control. In the 2nd Edition of Robot Modeling and Control, students will cover the theoretical fundamentals and the latest technological advances inrobot kinematics. With so much advancement in technology, from robotics to motion planning, society can implement more powerful and dynamic algorithms than ever before. This in-depth reference guide educates readers in four distinct parts; the first two serve as a guide to the fundamentals of robotics and motion control, while the last two dive more in-depth into control theory and nonlinear system analysis. With the new edition, readers gain access to new case studies and thoroughly researched information covering topics such as: Motion-planning, collision avoidance, trajectory optimization, and control of robots. Popular topics within the robotics industry and how they apply to various technologies. An expanded set of examples, simulations, problems, and case studies. Open-ended suggestions for students to apply the knowledge to real-life situations. A four-part reference essential for both undergraduate and graduate students, Robot Modeling and Control serves as a foundation for a solid education in robotics and motion planning"--

A New Edition Featuring Case Studies and Examples of the Fundamentals of Robot Kinematics, Dynamics, and Control

In the 2nd Edition of Robot Modeling and Control, students will cover the theoretical fundamentals and the latest technological advances in robot kinematics. With so much advancement in technology, from robotics to motion planning, society can implement more powerful and dynamic algorithms than ever before. This in-depth reference guide educates readers in four distinct parts; the first two serve as a guide to the fundamentals of robotics and motion control, while the last two dive more in-depth into control theory and nonlinear system analysis.

With the new edition, readers gain access to new case studies and thoroughly researched information covering topics such as: 

&;      Motion-planning, collision avoidance, trajectory optimization, and control of robots

&;      Popular topics within the robotics industry and how they apply to various technologies

&;      An expanded set of examples, simulations, problems, and case studies

&;      Open-ended suggestions for students to apply the knowledge to real-life situations

A four-part reference essential for both undergraduate and graduate students, Robot Modeling and Control serves as a foundation for a solid education in robotics and motion planning.

Preface v
Contents xi
1 Introduction
x
1.1 Mathematical Modeling of Robots
5(2)
1.1.1 Symbolic Representation of Robot Manipulators
5(1)
1.1.2 The Configuration Space
5(1)
1.1.3 The State Space
6(1)
1.1.4 The Workspace
7(1)
1.2 Robots as Mechanical Devices
7(6)
1.2.1 Classification of Robotic Manipulators
8(2)
1.2.2 Robotic Systems
10(1)
1.2.3 Accuracy and Repeatability
10(2)
1.2.4 Wrists and End Effectors
12(1)
1.3 Common Kinematic Arrangements
13(5)
1.3.1 Articulated Manipulator (RRR)
13(1)
1.3.2 Spherical Manipulator (RRP)
14(1)
1.3.3 SCARA Manipulator (RRP)
14(1)
1.3.4 Cylindrical Manipulator (RPP)
15(1)
1.3.5 Cartesian Manipulator (PPP)
15(3)
1.3.6 Parallel Manipulator
18(1)
1.4 Outline of the Text
18(15)
1.4.1 Manipulator Arms
18(9)
1.4.2 Underactuated and Mobile Robots
27(1)
Problems
27(2)
Notes and References
29(4)
I THE GEOMETRY OF ROBOTS
33(130)
2 Rigid Motions
35(40)
2.1 Representing Positions
36(2)
2.2 Representing Rotations
38(6)
2.2.1 Rotation in the Plane
38(3)
2.2.2 Rotations in Three Dimensions
41(3)
2.3 Rotational Transformations
44(4)
2.4 Composition of Rotations
48(4)
2.4.1 Rotation with Respect to the Current Frame
48(2)
2.4.2 Rotation with Respect to the Fixed Frame
50(1)
2.4.3 Rules for Composition of Rotations
51(1)
2.5 Parameterizations of Rotations
52(9)
2.5.1 Euler Angles
53(2)
2.5.2 Roll, Pitch, Yaw Angles
55(2)
2.5.3 Axis-Angle Representation
57(2)
2.5.4 Exponential Coordinates
59(2)
2.6 Rigid Motions
61(4)
2.6.1 Homogeneous Transformations
62(3)
2.6.2 Exponential Coordinates for General Rigid Motions
65(1)
2.7
Chapter Summary
65(10)
Problems
67(6)
Notes and References
73(2)
3 Forward Kinematics
75(26)
3.1 Kinematic Chains
75(3)
3.2 The Denavit-Haxtenberg Convention
78(9)
3.2.1 Existence and Uniqueness
80(3)
3.2.2 Assigning the Coordinate Frames
83(4)
3.3 Examples
87(9)
3.3.1 Planar Elbow Manipulator
87(2)
3.3.2 Three-Link Cylindrical Robot
89(1)
3.3.3 The Spherical Wrist
90(1)
3.3.4 Cylindrical Manipulator with Spherical Wrist
91(2)
3.3.5 Stanford Manipulator
93(2)
3.3.6 SCARA Manipulator
95(1)
3.4
Chapter Summary
96(5)
Problems
96(3)
Notes and References
99(2)
4 Velocity Kinematics
101(40)
4.1 Angular Velocity: The Fixed Axis Case
102(1)
4.2 Skew-Symmetric Matrices
103(4)
4.2.1 Properties of Skew-Symmetric Matrices
104(1)
4.2.2 The Derivative of a Rotation Matrix
105(2)
4.3 Angular Velocity: The General Case
107(1)
4.4 Addition of Angular Velocities
108(2)
4.5 Linear Velocity of a Point Attached to a Moving Frame
110(1)
4.6 Derivation of the Jacobian
111(8)
4.6.1 Angular Velocity
112(1)
4.6.2 Linear Velocity
113(2)
4.6.3 Combining the Linear and Angular Velocity Jacobians
115(4)
4.7 The Tool Velocity
119(2)
4.8 The Analytical Jacobian
121(1)
4.9 Singularities
122(7)
4.9.1 Decoupling of Singularities
123(2)
4.9.2 Wrist Singularities
125(1)
4.9.3 Arm Singularities
125(4)
4.10 Static Force/Torque Relationships
129(2)
4.11 Inverse Velocity and Acceleration
131(2)
4.12 Manipulability
133(3)
4.13
Chapter Summary
136(5)
Problems
138(2)
Notes and References
140(1)
5 Inverse Kinematics
141(22)
5.1 The General Inverse Kinematics Problem
141(2)
5.2 Kinematic Decoupling
143(2)
5.3 Inverse Position: A Geometric Approach
145(6)
5.3.1 Spherical Configuration
146(2)
5.3.2 Articulated Configuration
148(3)
5.4 Inverse Orientation
151(5)
5.5 Numerical Inverse Kinematics
156(2)
5.6
Chapter Summary
158(5)
Problems
160(2)
Notes and References
162(1)
II DYNAMICS AND MOTION PLANNING
163(106)
6 Dynamics
165(50)
6.1 The Euler---Lagrange Equations
166(11)
6.1.1 Motivation
166(4)
6.1.2 Holonomic Constraints and Virtual Work
170(4)
6.1.3 D'Alembert's Principle
174(3)
6.2 Kinetic and Potential Energy
177(4)
6.2.1 The Inertia Tensor
178(2)
6.2.2 Kinetic Energy for an n-Link Robot
180(1)
6.2.3 Potential Energy for an n-Link Robot
181(1)
6.3 Equations of Motion
181(3)
6.4 Some Common Configurations
184(10)
6.5 Properties of Robot Dynamic Equations
194(4)
6.5.1 Skew Symmetry and Passivity
194(2)
6.5.2 Bounds on the Inertia Matrix
196(1)
6.5.3 Linearity in the Parameters
196(2)
6.6 Newton-Euler Formulation
198(11)
6.6.1 Planar Elbow Manipulator Revisited
206(3)
6.7
Chapter Summary
209(6)
Problems
211(3)
Notes and References
214(1)
7 Path and Trajectory Planning
215(54)
7.1 The Configuration Space
216(6)
7.1.1 Representing the Configuration Space
217(1)
7.1.2 Configuration Space Obstacles
218(3)
7.1.3 Paths in the Configuration Space
221(1)
7.2 Path Planning for Q = R2
222(7)
7.2.1 The Visibility Graph
224(2)
7.2.2 The Generalized Voronoi Diagram
226(1)
7.2.3 Trapezoidal Decompositions
226(3)
7.3 Artificial Potential Fields
229(16)
7.3.1 Artificial Potential Fields for Q = Rn
230(5)
7.3.2 Potential Fields for Q ≠ Rn
235(10)
7.4 Sampling-Based Methods
245(7)
7.4.1 Probabilistic Roadmaps (PRM)
246(4)
7.4.2 Rapidly-Exploring Random Trees (RRTs)
250(2)
7.5 Trajectory Planning
252(11)
7.5.1 Trajectories for Point-to-Point Motion
253(8)
7.5.2 Trajectories for Paths Specified by Via Points
261(2)
7.6
Chapter Summary
263(6)
Problems
265(2)
Notes and References
267(2)
III CONTROL OF MANIPULATORS
269(168)
8 Independent Joint Control
271(40)
8.1 Introduction
271(2)
8.2 Actuator Dynamics
273(3)
8.3 Load Dynamics
276(2)
8.4 Independent Joint Model
278(3)
8.5 PID Control
281(7)
8.6 Feedforward Control
288(4)
8.6.1 Trajectory Tracking
289(2)
8.6.2 The Method of Computed Torque
291(1)
8.7 Drive-Train Dynamics
292(5)
8.8 State Space Design
297(7)
8.8.1 State Feedback Control
299(2)
8.8.2 Observers
301(3)
8.9
Chapter Summary
304(7)
Problems
307(2)
Notes and References
309(2)
9 Nonlinear and Multivariable Control
311(34)
9.1 Introduction
311(2)
9.2 PD Control Revisited
313(4)
9.3 Inverse Dynamics
317(12)
9.3.1 Joint Space Inverse Dynamics
317(3)
9.3.2 Task Space Inverse Dynamics
320(2)
9.3.3 Robust Inverse Dynamics
322(5)
9.3.4 Adaptive Inverse Dynamics
327(2)
9.4 Passivity-Based Control
329(4)
9.4.1 Passivity-Based Robust Control
331(1)
9.4.2 Passivity-Based Adaptive Control
332(1)
9.5 Torque Optimization
333(4)
9.6
Chapter Summary
337(8)
Problems
341(2)
Notes and References
343(2)
10 Force Control
345(20)
10.1 Coordinate Frames and Constraints
347(4)
10.1.1 Reciprocal Bases
347(2)
10.1.2 Natural and Artificial Constraints
349(2)
10.2 Network Models and Impedance
351(4)
10.2.1 Impedance Operators
353(1)
10.2.2 Classification of Impedance Operators
354(1)
10.2.3 Thevenin and Norton Equivalents
355(1)
10.3 Task Space Dynamics and Control
355(6)
10.3.1 Impedance Control
356(2)
10.3.2 Hybrid Impedance Control
358(3)
10.4
Chapter Summary
361(4)
Problems
362(2)
Notes and References
364(1)
11 Vision-Based Control
365(44)
11.1 Design Considerations
366(2)
11.1.1 Camera Configuration
366(1)
11.1.2 Image-Based vs. Position-Based Approaches
367(1)
11.2 Computer Vision for Vision-Based Control
368(10)
11.2.1 The Geometry of Image Formation
369(4)
11.2.2 Image Features
373(5)
11.3 Camera Motion and the Interaction Matrix
378(1)
11.4 The Interaction Matrix for Point Features
379(7)
11.4.1 Velocity Relative to a Moving Frame
380(1)
11.4.2 Constructing the Interaction Matrix
381(3)
11.4.3 Properties of the Interaction Matrix for Points
384(1)
11.4.4 The Interaction Matrix for Multiple Points
385(1)
11.5 Image-Based Control Laws
386(7)
11.5.1 Computing Camera Motion
387(2)
11.5.2 Proportional Control Schemes
389(1)
11.5.3 Performance of Image-Based Control Systems
390(3)
11.6 End Effector and Camera Motions
393(1)
11.7 Partitioned Approaches
394(3)
11.8 Motion Perceptibility
397(2)
11.9 Summary
399(10)
Problems
401(4)
Notes and References
405(4)
12 Feedback Linearization
409(28)
12.1 Background
410(7)
12.1.1 Manifolds, Vector Fields, and Distributions
410(4)
12.1.2 The Frobenius Theorem
414(3)
12.2 Feedback Linearization
417(2)
12.3 Single-Input Systems
419(10)
12.4 Multi-Input Systems
429(4)
12.5
Chapter Summary
433(4)
Problems
433(2)
Notes and References
435(2)
IV CONTROL OF UNDERACTUATED SYSTEMS
437(86)
13 Underactuated Robots
439(40)
13.1 Introduction
439(1)
13.2 Modeling
440(3)
13.3 Examples of Underactuated Robots
443(5)
13.3.1 The Cart-Pole System
443(2)
13.3.2 The Acrobot
445(1)
13.3.3 The Pendubot
446(1)
13.3.4 The Reaction-Wheel Pendulum
447(1)
13.4 Equilibria and Linear Controllability
448(8)
13.4.1 Linear Controllability
450(6)
13.5 Partial Feedback Linearization
456(5)
13.5.1 Collocated Partial Feedback Linearization
457(2)
13.5.2 Noncollocated Partial Feedback Linearization
459(2)
13.6 Output Feedback Linearization
461(5)
13.6.1 Computation of the Zero Dynamics
463(3)
13.6.2 Virtual Holonomic Constraints
466(1)
13.7 Passivity-Based Control
466(8)
13.7.1 The Simple Pendulum
467(4)
13.7.2 The Reaction-Wheel Pendulum
471(2)
13.7.3 Swingup and Balance of The Acrobot
473(1)
13.8
Chapter Summary
474(5)
Problems
476(1)
Notes and References
477(2)
14 Mobile Robots
479(44)
14.1 Nonholonomic Constraints
480(4)
14.2 Involutivity and Holonomy
484(3)
14.3 Examples of Nonholonomic Systems
487(6)
14.4 Dynamic Extension
493(2)
14.5 Controllability of Driftless Systems
495(4)
14.6 Motion Planning
499(10)
14.6.1 Conversion to Chained Forms
499(7)
14.6.2 Differential Flatness
506(3)
14.7 Feedback Control of Driftless Systems
509(10)
14.7.1 Stabilizability
509(2)
14.7.2 Nonsmooth Control
511(2)
14.7.3 Trajectory Tracking
513(2)
14.7.4 Feedback Linearization
515(4)
14.8
Chapter Summary
519(4)
Problems
520(1)
Notes and References
521(2)
A TRIGONOMETRY
523(2)
A.1 The Two-Argument Arctangent Function
523(1)
A.2 Useful Trigonometric Formulas
523(2)
B LINEAR ALGEBRA
525(14)
B.1 Vectors
525(1)
B.2 Inner Product Spaces
526(2)
B.3 Matrices
528(2)
B.4 Eigenvalues and Eigenvectors
530(3)
B.5 Differentiation of Vectors
533(1)
B.6 The Matrix Exponential
534(1)
B.7 Lie Groups and Lie Algebras
534(2)
B.8 Matrix Pseudoinverse
536(1)
B.9 Schur Complement
536(1)
B.10 Singular Value Decomposition (SVD)
537(2)
C LYAPUNOV STABILITY
539(12)
C.1 Continuity and Differentiability
539(2)
C.2 Vector Fields and Equilibria
541(4)
C.3 Lyapunov Functions
545(1)
C.4 Stability Criteria
545(1)
C.5 Global and Exponential Stability
546(1)
C.6 Stability of Linear Systems
547(1)
C.7 LaSalle's Theorem
548(1)
C.8 Barbalat's Lemma
549(2)
D OPTIMIZATION
551(4)
D.1 Unconstrained Optimization
551(1)
D.2 Constrained Optimization
552(3)
E CAMERA CALIBRATION
555(6)
E.1 The Image Plane and the Sensor Array
555(1)
E.2 Extrinsic Camera Parameters
556(1)
E.3 Intrinsic Camera Parameters
557(1)
E.4 Determining the Camera Parameters
557(4)
Bibliography 561(15)
Index 576
MARK W. SPONG has been researching and teaching robotics for over 35 years. He currently serves as a Professor, Excellence in Education Chair, in the Department of Systems Engineering at the University of Texas at Dallas. He has been recognized for outstanding achievements including the John R. Ragazzini Award for Control Education and the IEEE RAS Pioneer in Robotics Award. He is currently a Fellow of both IEEE and IFAC.

SETH HUTCHINSON received his Ph.D. from Purdue University in 1988, and is currently Professor and KUKA Chair for Robotics in the School of Interactive Computing at the Georgia Institute of Technology, where he also serves as Executive Director of the Institute for Robotics and Intelligent Machines. He was the Founding Editor-in-Chief of the IEEE Robotics and Automation Society's Conference Editorial Board, Editor-in-Chief of the IEEE Transactions on Robotics, and is a Fellow of the IEEE. His research in robotics spans the areas of planning, sensing, and control.

MATHUKUMALLI VIDYASAGAR received his Ph.D. in electrical engineering in 1969 from the University of Wisconsin in Madison. During his fifty-year career, he has worked in control theory, machine learning, robotics and cancer biology. Among the many honors he has received are Fellowship in The Royal Society and the IEEE Control Systems Award. At present he is a Distinguished Professor at the Indian Institute of Technology Hyderabad.