Introduction to Autonomous Robots

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
I MECHANISMS 9
2 Locomotion, Manipulation, and Their Representations 11
3 Kinematics 27
4 Forces 53
5 Grasping 61
II SENSING AND ACTUATION 71
6 Actuators 73
7 Sensors 81
III COMPUTATION 95
8 Vision 97
9 Feature Extraction 109
10 Artificial Neural Networks 119
11 Task Execution 139
12 Mapping 155
13 Path Planning 165
14 Manipulation 179
IV UNCERTAINTY 189
15 Uncertainty and Error Propagation 191
16 Localization 201
17 Simultaneous Localization and Mapping 219
V APPENDIXES
A Trigonometry 233
B Linear Algebra 235
C Statistics 239
D Backpropagation 247
E How to Write a Research Paper 253
F Sample Curricula 257
References 265
Index 269Nikolaus Correll is Associate Professor of Computer Science at the University of Colorado Boulder. Bradley Hayes is Assistant Professor of Computer Science at the University of Colorado Boulder. Christoffer Heckman is Assistant Professor of Computer Science at the University of Colorado Boulder. Alessandro Roncone is Assistant Professor of Computer Science at the University of Colorado Boulder.