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Introduction to Autonomous Robots: Mechanisms, Sensors, Acutators, and Algorithms [Kõva köide]

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  • Formaat: Hardback, 360 pages, kõrgus x laius: 229x178 mm, 86 black and white illustrations
  • Ilmumisaeg: 20-Dec-2022
  • Kirjastus: MIT Press
  • ISBN-10: 0262047551
  • ISBN-13: 9780262047555
Teised raamatud teemal:
Introduction to Autonomous Robots: Mechanisms, Sensors, Acutators, and AlgorithmsSuurem pilt
  • Formaat: Hardback, 360 pages, kõrgus x laius: 229x178 mm, 86 black and white illustrations
  • Ilmumisaeg: 20-Dec-2022
  • Kirjastus: MIT Press
  • ISBN-10: 0262047551
  • ISBN-13: 9780262047555
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780262047555.html
Märksõnad:
A comprehensive introduction to the field of autonomous robotics aimed at upper-level undergraduates and offering additional online resources.

Textbooks that provide a broad algorithmic perspective on the mechanics and dynamics of robots almost unfailingly serve students at the graduate level. Introduction to Autonomous Robots offers a much-needed resource for teaching third- and fourth-year undergraduates the computational fundamentals behind the design and control of autonomous robots. The authors use a class-tested and accessible approach to present progressive, step-by-step development concepts, alongside a wide range of real-world examples and fundamental concepts in mechanisms, sensing and actuation, computation, and uncertainty. Throughout, the authors balance the impact of hardware (mechanism, sensor, actuator) and software (algorithms) in teaching robot autonomy.
 
Features:
  •  Rigorous and tested in the classroom
  •  Written for engineering and computer science undergraduates with a sophomore-level understanding of linear algebra, probability theory, trigonometry, and statistics
  •  QR codes in the text guide readers to online lecture videos and animations
  •  Topics include: basic concepts in robotic mechanisms like locomotion and grasping, plus the resulting forces; operation principles of sensors and actuators; basic algorithms for vision and feature detection; an introduction to artificial neural networks, including convolutional and recurrent variants
  •  Extensive appendices focus on project-based curricula, pertinent areas of mathematics, backpropagation, writing a research paper, and other topics
  •  A growing library of exercises in an open-source, platform-independent simulation (Webots)
Preface xv
1 Introduction
1(8)
1.1 Intelligence and Embodiment
1(1)
1.2 A Roboticists' Problem
2(1)
1.3 Ratslife: An Example of Autonomous Mobile Robotics
3(2)
1.4 Autonomous Mobile Robots: Some Core Challenges
5(1)
1.5 Autonomous Manipulation: Some Core Challenges
5(4)
I MECHANISMS
9(62)
2 Locomotion, Manipulation, and Their Representations
11(16)
2.1 Locomotion and Manipulation Examples
11(2)
2.2 Static and Dynamic Stability
13(1)
2.3 Degrees of Freedom
14(4)
2.4 Coordinate Systems and Frames of Reference
18(9)
2.4.1 Matrix Notation
19(2)
2.4.2 Mapping from One Frame to Another
21(2)
2.4.3 Concatenation of Transformations
23(1)
2.4.4 Other Representations for Orientation
24(3)
3 Kinematics
27(26)
3.1 Forward Kinematics
28(5)
3.1.1 Forward Kinematics of a Simple Robot Arm
28(2)
3.1.2 The Denavit-Hartenberg Notation
30(3)
3.2 Inverse Kinematics
33(2)
3.2.1 Solvability
33(1)
3.2.2 Inverse Kinematics of a Simple Manipulator Arm
34(1)
3.3 Differential Kinematics
35(9)
3.3.1 Forward Differential Kinematics
36(1)
3.3.2 Forward Kinematics of a Differential-Wheel Robot
37(6)
3.3.3 Forward Kinematics of Carlike Steering
43(1)
3.4 Inverse Differential Kinematics
44(9)
3.4.1 Inverse Kinematics of Mobile Robots
46(2)
3.4.2 Feedback Control for Mobile Robots
48(1)
3.4.3 Under-actuation and Over-actuation
49(4)
4 Forces
53(8)
4.1 Statics
54(2)
4.2 Kineto-Statics Duality
56(1)
4.3 Manipulability
56(5)
4.3.1 Manipulability Ellipsoid in Velocity Space
56(1)
4.3.2 Manipulability Ellipsoid in Force Space
57(1)
4.3.3 Manipulability Considerations
58(3)
5 Grasping
61(10)
5.1 The Theory of Grasping
61(4)
5.1.1 Friction
62(1)
5.1.2 Multiple Contacts and Deformation
63(1)
5.1.3 Suction
64(1)
5.2 Simple Grasping Mechanisms
65(6)
5.2.1 1-DoF Scissorlike Gripper
65(1)
5.2.2 Parallel Jaw
66(1)
5.2.3 4-Bar Linkage Parallel Gripper
67(1)
5.2.4 Multifingered Hands
68(3)
II SENSING AND ACTUATION
71(24)
6 Actuators
73(8)
6.1 Electric Motors
73(4)
6.1.1 AC and DC Motors
74(1)
6.1.2 Stepper Motor
75(1)
6.1.3 Brushless DC Motor
76(1)
6.1.4 Servo Motor
76(1)
6.1.5 Motor Controllers
77(1)
6.2 Hydraulic and Pneumatic Actuators
77(2)
6.2.1 Hydraulic Actuators
78(1)
6.2.2 Pneumatic Actuators and Soft Robotics
78(1)
6.3 Safety Considerations
79(2)
7 Sensors
81(14)
7.1 Terminology
82(2)
7.1.1 Proprioception versus Exteroception
84(1)
7.2 Sensors That Measure the Robot's Joint Configuration
84(1)
7.3 Sensors That Measure Ego-Motion
85(2)
7.3.1 Accelerometers
85(1)
7.3.2 Gyroscopes
86(1)
7.4 Measuring Force
87(2)
7.4.1 Measuring Pressure or Touch
88(1)
7.5 Sensors to Measure Distance
89(3)
7.5.1 Reflection
89(1)
7.5.2 Phase Shift
90(1)
7.5.3 Time of Flight
91(1)
7.6 Sensors to Sense Global Pose
92(3)
III COMPUTATION
95(94)
8 Vision
97(12)
8.1 Images as Two-Dimensional Signals
97(1)
8.2 From Signals to Information
98(3)
8.3 Basic Image Operations
101(3)
8.3.1 Threshold-Based Operations
101(1)
8.3.2 Convolution-Based Filters
101(3)
8.3.3 Morphological Operations
104(1)
8.4 Extracting Structure from Vision
104(3)
8.5 Computer Vision and Machine Learning
107(2)
9 Feature Extraction
109(10)
9.1 Feature Detection as an Information-Reduction Problem
109(1)
9.2 Features
110(1)
9.3 Line Recognition
111(3)
9.3.1 Line Fitting Using Least Squares
111(1)
9.3.2 Split-and-Merge Algorithm
112(1)
9.3.3 RANSAC: Random Sample and Consensus
113(1)
9.3.4 The Hough Transform
114(1)
9.4 Scale-Invariant Feature Transforms
114(3)
9.4.1 Overview
115(1)
9.4.2 Object Recognition Using Scale-Invariant Features
116(1)
9.5 Feature Detection and Machine Learning
117(2)
10 Artificial Neural Networks
119(20)
10.1 The Simple Perceptron
120(3)
10.1.1 Geometric Interpretation of the Simple Perceptron
121(1)
10.1.2 Training the Simple Perceptron
122(1)
10.2 Activation Functions
123(1)
10.3 From the Simple Perceptron to Multilayer Neural Networks
124(3)
10.3.1 Formal Description of Artificial Neural Networks
124(2)
10.3.2 Training a Multilayer Neural Network
126(1)
10.4 From Single Outputs to Higher Dimensional Data
127(1)
10.5 Objective Functions and Optimization
128(3)
10.5.1 Loss Functions for Regression Tasks
128(1)
10.5.2 Loss Functions for Classification Tasks
129(1)
10.5.3 Binary and Categorical Cross-Entropy
130(1)
10.6 Convolutional Neural Networks
131(4)
10.6.1 From Convolutions to 2D Neural Networks
132(1)
10.6.2 Padding and Striding
133(1)
10.6.3 Pooling
133(1)
10.6.4 Flattening
134(1)
10.6.5 A Sample CNN
134(1)
10.6.6 Convolutional Networks beyond 2D Image Data
135(1)
10.7 Recurrent Neural Networks
135(4)
11 Task Execution
139(16)
11.1 Reactive Control
139(4)
11.1.1 Limitations of Reactive Control
141(2)
11.2 Finite State Machines
143(2)
11.2.1 Implementation
144(1)
11.3 Hierarchical Finite State Machines
145(2)
11.3.1 Implementation
145(2)
11.4 Behavior Trees
147(3)
11.4.1 Node Definition and Status
147(1)
11.4.2 Node Types
148(1)
11.4.3 Behavior Tree Execution
149(1)
11.4.4 Implementation
150(1)
11.5 Mission Planning
150(5)
11.5.1 The General Problem Solver and STRIPS
151(4)
12 Mapping
155(10)
12.1 Map Representations
156(1)
12.2 Iterative Closest Point for Sparse Mapping
157(3)
12.3 Octomap: Dense Mapping of Voxels
160(1)
12.4 RGB-D Mapping: Dense Mapping of Surfaces
160(5)
13 Path Planning
165(14)
13.1 The Configuration Space
165(1)
13.2 Graph-Based Planning Algorithms
166(3)
13.2.1 Dijkstra's Algorithm
166(2)
13.2.2 A*
168(1)
13.3 Sampling-Based Path Planning
169(4)
13.3.1 Rapidly Exploring Random Trees
170(3)
13.4 Planning at Different Length Scales
173(2)
13.5 Coverage Path Planning
175(1)
13.6 Summary and Outlook
175(4)
14 Manipulation
179(10)
14.1 Nonprehensile Manipulation
179(1)
14.2 Choosing the Right Grasp
180(4)
14.2.1 Finding Good Grasps for Simple Grippers
181(2)
14.2.2 Finding Good Grasps for Multifingered Hands
183(1)
14.3 Pick and Place
184(1)
14.4 Peg-in-Hole Problems
185(4)
IV UNCERTAINTY
189(42)
15 Uncertainty and Error Propagation
191(10)
15.1 Uncertainty in Robotics as a Random Variable
191(1)
15.2 Error Propagation
192(4)
15.2.1 Example: Line Fitting
194(1)
15.2.2 Example: Odometry
195(1)
15.3 Optimal Sensor Fusion
196(5)
15.3.1 The Kalman Filter
197(4)
16 Localization
201(18)
16.1 Motivating Example
201(2)
16.2 Markov Localization
203(4)
16.2.1 Perception Update
203(1)
16.2.2 Action Update
204(1)
16.2.3 Example: Markov Localization on a Topological Map
205(2)
16.3 The Bayes Filter
207(4)
16.3.1 Example: Bayes Filter on a Grid
209(2)
16.4 Particle Filter
211(2)
16.5 Extended Kalman Filter
213(3)
16.5.1 Odometry Using the Kalman Filter
214(2)
16.6 Summary: Probabilistic Map-Based Localization
216(3)
17 Simultaneous Localization and Mapping
219(12)
17.1 Introduction
219(2)
17.1.1 Landmarks
220(1)
17.1.2 Special Case I: One Landmark
220(1)
17.1.3 Special Case II: Two Landmarks
221(1)
17.2 The Covariance Matrix
221(1)
17.3 EKF SLAM
222(3)
17.3.1 Algorithm
222(2)
17.3.2 Multiple Sensors
224(1)
17.4 Graph-Based SLAM
225(6)
17.4.1 SLAM as a Maximum-Likelihood Estimation Problem
225(3)
17.4.2 Numerical Techniques for Graph-Based SLAM
228(3)
V APPENDIXES
231(34)
A Trigonometry
233(2)
A.1 Inverse Trigonometry
234(1)
A.2 Trigonometric Identities
234(1)
B Linear Algebra
235(4)
B.1 Dot Product
235(1)
B.2 Cross Product
235(1)
B.3 Matrix Product
236(1)
B.4 Matrix Inversion
236(1)
B.5 Principal Component Analysis
237(2)
C Statistics
239(2)
C.1 Random Variables and Probability Distributions
239(1)
C.1.1 The Normal Distribution
240(1)
C.1.2 Normal Distribution in Two Dimensions
241(1)
C.2 Conditional Probabilities and Bayes' Rule
241(1)
C.3 Sum of Two Random Processes
242(1)
C.4 Linear Combinations of Independent Gaussian Random Variables
242(1)
C.5 Testing Statistical Significance
242(5)
C.5.1 Null Hypothesis on Distributions
243(1)
C.5.2 Testing Whether Two Distributions Are Independent
244(1)
C.5.3 Statistical Significance of True-False Tests
245(1)
C.5.4 Summary
245(2)
D Baekpropagarion
247(6)
D.1 Backward Propagation of Error
249(2)
D.2 Backpropagation Algorithm
251(2)
E How to Write a Research Paper
253(4)
E.1 Original Research
253(2)
E.2 Hypothesis: Or, What Do We Learn from This Work?
255(1)
E.3 Survey and Tutorial
256(1)
E.4 Writing It Up!
256(1)
F Sample Curricula
257(3)
F.1 An Introduction to Autonomous Mobile Robots
257(1)
F.1.1 Overview
257(1)
F.1.2 Content
258(1)
F.1.3 Implementation Suggestions
259(1)
F.2 An Introduction to Robotic Manipulation
260(2)
F.2.1 Overview
260(1)
F.2.2 Content
261(1)
F.2.3 Implementation Suggestions
261(1)
F.3 An Introduction to Robotic Systems
262(3)
F.3.1 Overview
262(1)
F.3.2 Content
262(1)
F.3.3 Implementation Suggestions
263(1)
F.4 Class Debates
263(2)
References 265(4)
Index 269