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E-raamat: Introduction to Autonomous Mobile Robots

(University of Zurich), (Carnegie Mellon University), (Autonomous Systems Lab)
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Mobile robots range from the Mars Pathfinder mission’s teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory.

The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms.
Acknowledgments xiii
Preface xv
1 Introduction
1(12)
1.1 Introduction
1(10)
1.2 An Overview of the Book
11(2)
2 Locomotion
13(44)
2.1 Introduction
13(4)
2.1.1 Key issues for locomotion
16(1)
2.2 Legged Mobile Robots
17(18)
2.2.1 Leg configurations and stability
18(3)
2.2.2 Consideration of dynamics
21(4)
2.2.3 Examples of legged robot locomotion
25(10)
2.3 Wheeled Mobile Robots
35(15)
2.3.1 Wheeled locomotion: The design space
35(8)
2.3.2 Wheeled locomotion: Case studies
43(7)
2.4 Aerial Mobile Robots
50(6)
2.4.1 Introduction
50(2)
2.4.2 Aircraft configurations
52(1)
2.4.3 State of the art in autonomous VTOL
52(4)
2.5 Problems
56(1)
3 Mobile Robot Kinematics
57(44)
3.1 Introduction
57(1)
3.2 Kinematic Models and Constraints
58(19)
3.2.1 Representing robot position
58(3)
3.2.2 Forward kinematic models
61(2)
3.2.3 Wheel kinematic constraints
63(8)
3.2.4 Robot kinematic constraints
71(2)
3.2.5 Examples: Robot kinematic models and constraints
73(4)
3.3 Mobile Robot Maneuverability
77(7)
3.3.1 Degree of mobility
77(4)
3.3.2 Degree of steerability
81(1)
3.3.3 Robot maneuverability
82(2)
3.4 Mobile Robot Workspace
84(6)
3.4.1 Degrees of freedom
84(1)
3.4.2 Holonomic robots
85(2)
3.4.3 Path and trajectory considerations
87(3)
3.5 Beyond Basic Kinematics
90(1)
3.6 Motion Control (Kinematic Control)
91(8)
3.6.1 Open loop control (trajectory-following)
91(1)
3.6.2 Feedback control
92(7)
3.7 Problems
99(2)
4 Perception
101(164)
4.1 Sensors for Mobile Robots
101(41)
4.1.1 Sensor classification
101(2)
4.1.2 Characterizing sensor performance
103(6)
4.1.3 Representing uncertainty
109(6)
4.1.4 Wheel/motor sensors
115(1)
4.1.5 Heading sensors
116(3)
4.1.6 Accelerometers
119(2)
4.1.7 Inertial measurement unit (IMU)
121(1)
4.1.8 Ground beacons
122(3)
4.1.9 Active ranging
125(15)
4.1.10 Motion/speed sensors
140(2)
4.1.11 Vision sensors
142(1)
4.2 Fundamentals of Computer Vision
142(53)
4.2.1 Introduction
142(1)
4.2.2 The digital camera
142(6)
4.2.3 Image formation
148(11)
4.2.4 Omnidirectional cameras
159(10)
4.2.5 Structure from stereo
169(11)
4.2.6 Structure from motion
180(9)
4.2.7 Motion and optical flow
189(3)
4.2.8 Color tracking
192(3)
4.3 Fundamentals of Image Processing
195(13)
4.3.1 Image filtering
196(3)
4.3.2 Edge detection
199(8)
4.3.3 Computing image similarity
207(1)
4.4 Feature Extraction
208(4)
4.5 Image Feature Extraction: Interest Point Detectors
212(22)
4.5.1 Introduction
212(1)
4.5.2 Properties of the ideal feature detector
213(2)
4.5.3 Corner detectors
215(5)
4.5.4 Invariance to photometric and geometric changes
220(7)
4.5.5 Blob detectors
227(7)
4.6 Place Recognition
234(8)
4.6.1 Introduction
234(1)
4.6.2 From bag of features to visual words
235(1)
4.6.3 Efficient location recognition by using an inverted file
236(1)
4.6.4 Geometric verification for robust place recognition
237(1)
4.6.5 Applications
237(1)
4.6.6 Other image representations for place recognition
238(4)
4.7 Feature Extraction Based on Range Data (Laser, Ultrasonic)
242(20)
4.7.1 Line fitting
243(5)
4.7.2 Six line-extraction algorithms
248(11)
4.7.3 Range histogram features
259(1)
4.7.4 Extracting other geometric features
260(2)
4.8 Problems
262(3)
5 Mobile Robot Localization
265(104)
5.1 Introduction
265(1)
5.2 The Challenge of Localization: Noise and Aliasing
266(9)
5.2.1 Sensor noise
267(1)
5.2.2 Sensor aliasing
268(1)
5.2.3 Effector noise
269(1)
5.2.4 An error model for odometric position estimation
270(5)
5.3 To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions
275(3)
5.4 Belief Representation
278(6)
5.4.1 Single-hypothesis belief
278(2)
5.4.2 Multiple-hypothesis belief
280(4)
5.5 Map Representation
284(12)
5.5.1 Continuous representations
284(3)
5.5.2 Decomposition strategies
287(7)
5.5.3 State of the art: Current challenges in map representation
294(2)
5.6 Probabilistic Map-Based Localization
296(46)
5.6.1 Introduction
296(1)
5.6.2 The robot localization problem
297(2)
5.6.3 Basic concepts of probability theory
299(3)
5.6.4 Terminology
302(2)
5.6.5 The ingredients of probabilistic map-based localization
304(2)
5.6.6 Classification of localization problems
306(1)
5.6.7 Markov localization
307(15)
5.6.8 Kalman filter localization
322(20)
5.7 Other Examples of Localization Systems
342(6)
5.7.1 Landmark-based navigation
344(1)
5.7.2 Globally unique localization
345(1)
5.7.3 Positioning beacon systems
346(1)
5.7.4 Route-based localization
347(1)
5.8 Autonomous Map Building
348(18)
5.8.1 Introduction
348(1)
5.8.2 SLAM: The simultaneous localization and mapping problem
349(2)
5.8.3 Mathematical definition of SLAM
351(2)
5.8.4 Extended Kalman Filter (EKF) SLAM
353(3)
5.8.5 Visual SLAM with a single camera
356(3)
5.8.6 Discussion on EKF SLAM
359(2)
5.8.7 Graph-based SLAM
361(2)
5.8.8 Particle filter SLAM
363(1)
5.8.9 Open challenges in SLAM
364(1)
5.8.10 Open source SLAM software and other resources
365(1)
5.9 Problems
366(3)
6 Planning and Navigation
369(56)
6.1 Introduction
369(1)
6.2 Competences for Navigation: Planning and Reacting
370(1)
6.3 Path Planning
371(22)
6.3.1 Graph search
373(13)
6.3.2 Potential field path planning
386(7)
6.4 Obstacle avoidance
393(16)
6.4.1 Bug algorithm
393(4)
6.4.2 Vector field histogram
397(2)
6.4.3 The bubble band technique
399(2)
6.4.4 Curvature velocity techniques
401(1)
6.4.5 Dynamic window approaches
402(2)
6.4.6 The Schlegel approach to obstacle avoidance
404(1)
6.4.7 Nearness diagram
405(1)
6.4.8 Gradient method
405(1)
6.4.9 Adding dynamic constraints
406(1)
6.4.10 Other approaches
406(1)
6.4.11 Overview
406(3)
6.5 Navigation Architectures
409(14)
6.5.1 Modularity for code reuse and sharing
410(1)
6.5.2 Control localization
410(1)
6.5.3 Techniques for decomposition
411(5)
6.5.4 Case studies: tiered robot architectures
416(7)
6.6 Problems
423(2)
Bibliography
425(22)
Books
425(2)
Papers
427(17)
Referenced Webpages
444(3)
Index 447