Introduction to Autonomous Robots

Mechanisms, Sensors, Actuators, and Algorithms

by Correll, Hayes, Heckman, Roncone

ISBN: 9780262372954 | Copyright 2022

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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.

Rigorous and tested in the classroom, Introduction to Autonomous Robots is written for engineering and computer science undergraduates with a sophomore-level understanding of linear algebra, probability theory, trigonometry, and statistics. The text covers topics like 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. The authors have included QR codes in the text guide readers to online lecture videos and animations. The book also features extensive appendices focus on project-based curricula, pertinent areas of mathematics, backpropagation, writing a research paper, and other topics, and is accompanied by a growing library of exercises in an open-source, platform-independent simulation (Webots).

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Contents (pg. vii)
Preface (pg. xv)
1. Introduction (pg. 1)
1.1 Intelligence and Embodiment (pg. 1)
1.2 A Roboticists' Problem (pg. 2)
1.3 Ratslife: An Example of Autonomous Mobile Robotics (pg. 3)
1.4 Autonomous Mobile Robots: Some Core Challenges (pg. 5)
1.5 Autonomous Manipulation: Some Core Challenges (pg. 5)
I. Mechanisms (pg. 9)
2. Locomotion, Manipulation, and Their Representations (pg. 11)
2.1 Locomotion and Manipulation Examples (pg. 11)
2.2 Static and Dynamic Stability (pg. 13)
2.3 Degrees of Freedom (pg. 14)
2.4 Coordinate Systems and Frames of Reference (pg. 18)
3. Kinematics (pg. 27)
3.1 Forward Kinematics (pg. 28)
3.2 Inverse Kinematics (pg. 33)
3.3 Differential Kinematics (pg. 35)
3.4 Inverse Differential Kinematics (pg. 44)
4. Forces (pg. 53)
4.1 Statics (pg. 54)
4.2 Kineto-Statics Duality (pg. 56)
4.3 Manipulability (pg. 56)
5. Grasping (pg. 61)
5.1 The Theory of Grasping (pg. 61)
5.2 Simple Grasping Mechanisms (pg. 65)
II. Sensing and Actuation (pg. 71)
6. Actuators (pg. 73)
6.1 Electric Motors (pg. 73)
6.2 Hydraulic and Pneumatic Actuators (pg. 77)
6.3 Safety Considerations (pg. 79)
7. Sensors (pg. 81)
7.1 Terminology (pg. 82)
7.2 Sensors That Measure the Robot's Joint Configuration (pg. 84)
7.3 Sensors That Measure Ego-Motion (pg. 85)
7.4 Measuring Force (pg. 87)
7.5 Sensors to Measure Distance (pg. 89)
7.6 Sensors to Sense Global Pose (pg. 92)
III. Computation (pg. 95)
8. Vision (pg. 97)
8.1 Images as Two-Dimensional Signals (pg. 97)
8.2 From Signals to Information (pg. 98)
8.3 Basic Image Operations (pg. 101)
8.4 Extracting Structure from Vision (pg. 104)
8.5 Computer Vision and Machine Learning (pg. 107)
9. Feature Extraction (pg. 109)
9.1 Feature Detection as an Information-Reduction Problem (pg. 109)
9.2 Features (pg. 110)
9.3 Line Recognition (pg. 111)
9.4 Scale-Invariant Feature Transforms (pg. 114)
9.5 Feature Detection and Machine Learning (pg. 117)
10. Artificial Neural Networks (pg. 119)
10.1 The Simple Perceptron (pg. 120)
10.2 Activation Functions (pg. 123)
10.3 From the Simple Perceptron to Multilayer Neural Networks (pg. 124)
10.4 From Single Outputs to Higher Dimensional Data (pg. 127)
10.5 Objective Functions and Optimization (pg. 128)
10.6 Convolutional Neural Networks (pg. 131)
10.7 Recurrent Neural Networks (pg. 135)
11. Task Execution (pg. 139)
11.1 Reactive Control (pg. 139)
11.2 Finite State Machines (pg. 143)
11.3 Hierarchical Finite State Machines (pg. 145)
11.4 Behavior Trees (pg. 147)
11.5 Mission Planning (pg. 150)
12. Mapping (pg. 155)
12.1 Map Representations (pg. 156)
12.2 Iterative Closest Point for Sparse Mapping (pg. 157)
12.3 Octomap: Dense Mapping of Voxels (pg. 160)
12.4 RGB-D Mapping: Dense Mapping of Surfaces (pg. 160)
13. Path Planning (pg. 165)
13.1 The Configuration Space (pg. 165)
13.2 Graph-Based Planning Algorithms (pg. 166)
13.3 Sampling-Based Path Planning (pg. 169)
13.4 Planning at Different Length Scales (pg. 173)
13.5 Coverage Path Planning (pg. 175)
13.6 Summary and Outlook (pg. 175)
14. Manipulation (pg. 179)
14.1 Nonprehensile Manipulation (pg. 179)
14.2 Choosing the Right Grasp (pg. 180)
14.3 Pick and Place (pg. 184)
14.4 Peg-in-Hole Problems (pg. 185)
IV. Uncertainty (pg. 189)
15. Uncertainty and Error Propagation (pg. 191)
15.1 Uncertainty in Robotics as a Random Variable (pg. 191)
15.2 Error Propagation (pg. 192)
15.3 Optimal Sensor Fusion (pg. 196)
16. Localization (pg. 201)
16.1 Motivating Example (pg. 201)
16.2 Markov Localization (pg. 203)
16.3 The Bayes Filter (pg. 207)
16.4 Particle Filter (pg. 211)
16.5 Extended Kalman Filter (pg. 213)
16.6 Summary: Probabilistic Map-Based Localization (pg. 216)
17. Simultaneous Localization and Mapping (pg. 219)
17.1 Introduction (pg. 219)
17.2 The Covariance Matrix (pg. 221)
17.3 EKF SLAM (pg. 222)
17.4 Graph-Based SLAM (pg. 225)
V. Appendixes (pg. 231)
A. Trigonometry (pg. 233)
A.1 Inverse Trigonometry (pg. 234)
A.2 Trigonometric Identities (pg. 234)
B. Linear Algebra (pg. 235)
B.1 Dot Product (pg. 235)
B.2 Cross Product (pg. 235)
B.3 Matrix Product (pg. 236)
B.4 Matrix Inversion (pg. 236)
B.5 Principal Component Analysis (pg. 237)
C. Statistics (pg. 239)
C.1 Random Variables and Probability Distributions (pg. 239)
C.2 Conditional Probabilities and Bayes' Rule (pg. 241)
C.3 Sum of Two Random Processes (pg. 242)
C.4 Linear Combinations of Independent Gaussian Random Variables (pg. 242)
C.5 Testing Statistical Significance (pg. 242)
D. Backpropagation (pg. 247)
D.1 Backward Propagation of Error (pg. 249)
D.2 Backpropagation Algorithm (pg. 251)
E. How to Write a Research Paper (pg. 253)
E.1 Original Research (pg. 253)
E.2 Hypothesis: Or, What Do We Learn from This Work? (pg. 255)
E.3 Survey and Tutorial (pg. 256)
E.4 Writing It Up! (pg. 256)
F. Sample Curricula (pg. 257)
F.1 An Introduction to Autonomous Mobile Robots (pg. 257)
F.2 An Introduction to Robotic Manipulation (pg. 260)
F.3 An Introduction to Robotic Systems (pg. 262)
F.4 Class Debates (pg. 263)
References (pg. 265)
Index (pg. 269)

Nikolaus Correll

Nikolaus Correll is Associate Professor of Computer Science at the University of Colorado Boulder.

Bradley Hayes

Bradley Hayes is Assistant Professor of Computer Science at the University of Colorado Boulder.

Christoffer Heckman

Christoffer Heckman is Assistant Professor of Computer Science at the University of Colorado Boulder.

Alessandro Roncone

Alessandro Roncone is Assistant Professor of Computer Science at the University of Colorado Boulder.

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