[robotics-worldwide] [news] New Textbook on Mobile Robots

Alonzo Kelly alonzo at cmu.edu
Mon Dec 2 08:20:51 PST 2013


Colleagues:

My text on mobile robots is finally available. The publisher is Cambridge University Press.
You can order it from Amazon here:

http://www.amazon.com/Mobile-Robotics-Mathematics-Models-Methods/dp/110703115X

The book covers a lot of ground. Both my grad and undergrad courses are based on it, where the latter
covers the material at a less comprehensive level. Numerous exercises are included at the 
end of each chapter. 

The Table of Contents is provided below.

Regards
Al.

-----------------------------------------
CHAPTER 1 Introduction 	 	1

1.1 Applications of Mobile Robots 	2
1.2 Types of Mobile Robots 	2
1.2.1 Automated Guided Vehicles (AGVs) 	2
1.2.2 Service Robots 	3
1.2.3 Cleaning and Lawn Care Robots 	3
1.2.4 Social Robots 	4
1.2.5 Field Robots 	5
1.2.6 Inspection, Reconnaissance, Surveillance, and Exploration Robots 	6
1.3 Mobile Robot Engineering 	7
1.3.1 Mobile Robot Subsystems 	7
1.3.2 Overview of the Text 	7
1.3.3 Fundamentals of Wheeled Mobile Robots 	9
1.3.4 References and Further Reading 	11
1.3.5 Exercises 	11
CHAPTER 2 Math Fundamentals 	 	12

2.1 Conventions and Definitions 	12
2.1.1 Notational Conventions 	13
2.1.2 Embedded Coordinate Frames 	17
2.1.3 References and Further Reading 	21
2.2 Matrices 	21
2.2.1 Matrix Operations 	21
2.2.2 Matrix Functions 	24
2.2.3 Matrix Inversion 	25
2.2.4 Rank-Nullity Theorem 	28
2.2.5 Matrix Algebra 	29
2.2.6 Matrix Calculus 	31
2.2.7 Leibnitz’ Rule 	39
2.2.8 References and Further Reading 	40
2.2.9 Exercises 	40
2.3 Fundamentals of Rigid Transforms 	41
2.3.1 Definitions 	41
2.3.2 Why Homogeneous Transforms 	42
2.3.3 Semantics and Interpretations 	43
2.3.4 References and Further Reading 	55
2.3.5 Exercises 	56
2.4 Kinematics of Mechanisms 	57
2.4.1 Forward Kinematics 	57
2.4.2 Inverse Kinematics 	61
2.4.3 Differential Kinematics 	66
2.4.4 References and Further Reading 	69
2.4.5 Exercises 	69
2.5 Orientation and Angular Velocity 	70
2.5.1 Orientation in Euler Angle Form 	70
2.5.2  Angular Rates and Small Angles 	75
2.5.3 Angular Velocity and Orientation Rates in Euler Angle Form 	77
2.5.4 Angular Velocity and Orientation Rates in Angle-Axis Form 	78
2.5.5 References and Further Reading 	80
2.5.6 Exercises 	81
2.6 Kinematic Models of Sensors 	81
2.6.1 Kinematics of Video Cameras 	81
2.6.2 Kinematics of Laser Rangefinders 	83
2.6.3 References and Further Reading 	89
2.6.4 Exercises 	89
2.7 Transform Graphs & Pose Networks 	90
2.7.1 Transforms as Relationships 	90
2.7.2 Solving Pose Networks 	93
2.7.3 Overconstrained Networks 	95
2.7.4 Differential Kinematics Applied to Frames in General Position 	97
2.7.5 References and Further Reading 	102
2.7.6 Exercises 	103
2.8 Quaternions 	103
2.8.1 Representations and Notation 	104
2.8.2 Quaternion Multiplication 	105
2.8.3 Other Quaternion Operations 	107
2.8.4 Representing 3D Rotations 	109
2.8.5 Attitude and Angular Velocity 	111
2.8.6 References and Further Reading 	114
2.8.7 Exercises 	114
CHAPTER 3 Numerical Methods 	 	116

3.1 Linearization and Optimization of Functions of Vectors 	116
3.1.1 Linearization 	117
3.1.2 Optimization of Objective Functions 	120
3.1.3 Constrained Optimization 	124
3.1.4 References and Further Reading 	130
3.1.5 Exercises 	130
3.2 Systems of Equations 	131
3.2.1 Linear Systems 	131
3.2.2 Nonlinear Systems 	136
3.2.3 References and Further Reading 	138
3.2.4 Exercises 	139
3.3 Nonlinear and Constrained Optimization 	140
3.3.1 Nonlinear Optimization 	140
3.3.2 Constrained Optimization 	146
3.3.3 References and Further Reading 	150
3.3.4 Exercises 	150
3.4 Differential Algebraic Systems 	151
3.4.1 Constrained Dynamics 	151
3.4.2 First- and Second-Order Constrained Kinematic Systems 	154
3.4.3 Lagrangian Dynamics 	157
3.4.4 Constraints 	162
3.4.5 References and Further Reading 	166
3.4.6 Exercises 	167
3.5 Integration of Differential Equations 	168
3.5.1 Dynamic Models in State Space 	168
3.5.2 Integration of State Space Models 	168
3.5.3 References and Further Reading 	172
3.5.4 Exercises 	172
CHAPTER 4 Dynamics 	 	173

4.1 Moving Coordinate Systems 	173
4.1.1 Context of Measurement 	174
4.1.2 Change of Reference Frame 	175
4.1.3 Example: Attitude Stability Margin Estimation 	180
4.1.4 Recursive Transformations of State of Motion 	182
4.1.5 References and Further Reading 	186
4.1.6 Exercises 	186
4.2 Kinematics of Wheeled Mobile Robots 	187
4.2.1 Aspects of Rigid Body Motion 	187
4.2.2 WMR Velocity Kinematics for Fixed Contact Point 	191
4.2.3 Common Steering Configurations 	195
4.2.4 References and Further Reading 	200
4.2.5 Exercises 	201
4.3 Constrained Kinematics and Dynamics 	201
4.3.1 Constraints of Disallowed Direction 	202
4.3.2 Constraints of Rolling Without Slipping 	207
4.3.3 Lagrangian Dynamics 	211
4.3.4 Terrain Contact 	217
4.3.5 Trajectory Estimation and Prediction 	220
4.3.6 References and Further Reading 	224
4.3.7 Exercises 	225
4.4 Aspects of Linear Systems Theory 	226
4.4.1 Linear Time Invariant Systems 	227
4.4.2 State Space Representation of Linear Dynamical Systems 	234
4.4.3 Nonlinear Dynamical Systems 	239
4.4.4 Perturbative Dynamics of Nonlinear Dynamical Systems 	240
4.4.5 References and Further Reading 	244
4.4.6 Exercises 	244
4.5 Predictive Modeling and System Identification 	245
4.5.1 Braking 	245
4.5.2 Turning 	247
4.5.3 Vehicle Rollover 	250
4.5.4 Wheel Slip and Yaw Stability 	253
4.5.5 Parameterization and Linearization of Dynamic Models 	256
4.5.6 System Identification 	259
4.5.7 References and Further Reading 	268
4.5.8 Exercises 	269
CHAPTER 5 Optimal Estimation 	 	270

5.1 Random Variables, Processes, and Transformation 	270
5.1.1 Characterizing Uncertainty 	270
5.1.2 Random Variables 	272
5.1.3 Transformation of Uncertainty 	279
5.1.4 Random Processes 	289
5.1.5 References and Further Reading 	294
5.1.6 Exercises 	295
5.2 Covariance Propagation and Optimal Estimation 	296
5.2.1 Variance of Continuous Integration and Averaging Processes 	296
5.2.2 Stochastic Integration 	301
5.2.3 Optimal Estimation 	307
5.2.4 References and Further Reading 	315
5.2.5 Exercises 	315
5.3 State Space Kalman Filters 	316
5.3.1 Introduction 	316
5.3.2 Linear Discrete Time Kalman Filter 	319
5.3.3 Kalman Filters for Nonlinear Systems 	321
5.3.4 Simple Example: 2D Mobile Robot 	327
5.3.5 Pragmatic Information for Kalman Filters 	338
5.3.6 Other Forms of the Kalman Filter 	344
5.3.7 References and Further Reading 	344
5.3.8 Exercises 	345
5.4 Bayesian Estimation 	346
5.4.1 Definitions 	346
5.4.2 Bayes’ Rule 	349
5.4.3 Bayes’ Filters 	353
5.4.4 Bayesian Mapping 	358
5.4.5 Bayesian Localization 	365
5.4.6 References and Further Reading 	369
5.4.7 Exercises 	369
CHAPTER 6 State Estimation 	 	370

6.1 Mathematics of Pose Estimation 	370
6.1.1 Pose Fixing Versus Dead Reckoning 	371
6.1.2 Pose Fixing 	372
6.1.3 Error Propagation in Triangulation 	376
6.1.4 Real Pose Fixing Systems 	384
6.1.5 Dead Reckoning 	385
6.1.6 Real Dead Reckoning Systems 	396
6.1.7 References and Further Reading 	396
6.1.8 Exercises 	397
6.2 Sensors For State Estimation 	398
6.2.1 Articulation Sensors 	398
6.2.2 Ambient Field Sensors 	400
6.2.3 Inertial Frames of Reference 	401
6.2.4 Inertial Sensors 	403
6.2.5 References and Further Reading 	409
6.2.6 Exercises 	410
6.3  Inertial Navigation Systems 	410
6.3.1 Introduction 	410
6.3.2 Mathematics of Inertial Navigation 	411
6.3.3 Errors and Aiding in Inertial Navigation 	416
6.3.4 Example: Simple Odometry Aided Attitude and Heading Reference System 	420
6.3.5 References and Further Reading 	423
6.3.6 Exercises 	424
6.4 Satellite Navigation Systems 	425
6.4.1 Introduction 	425
6.4.2 Implementation 	425
6.4.3 State Measurement 	426
6.4.4 Performance 	430
6.4.5 Modes of Operation 	431
6.4.6 References and Further Reading 	433
6.4.7 Exercises 	434
CHAPTER 7 Control 	 	435

7.1 Classical Control 	435
7.1.1 Introduction 	435
7.1.2 Virtual Spring-Damper 	439
7.1.3 Feedback Control 	441
7.1.4 Model Referenced and Feedforward Control 	447
7.1.5 References and Further Reading 	452
7.1.6 Exercises 	452
7.2 State Space Control 	453
7.2.1 Introduction 	453
7.2.2 State Space Feedback Control 	454
7.2.3 Example: Robot Trajectory Following 	458
7.2.4 Perception Based Control 	463
7.2.5 Steering Trajectory Generation 	466
7.2.6 References and Further Reading 	472
7.2.7 Exercises 	472
7.3 Optimal and Model Predictive Control 	473
7.3.1 Calculus of Variations 	473
7.3.2 Optimal Control 	476
7.3.3 Model Predictive Control 	482
7.3.4 Techniques for Solving Optimal Control Problems 	484
7.3.5 Parametric Optimal Control 	487
7.3.6 References and Further Reading 	492
7.3.7 Exercises 	492
7.4 Intelligent Control 	493
7.4.1 Introduction 	493
7.4.2 Evaluation 	496
7.4.3 Representation 	499
7.4.4 Search 	507
7.4.5 References and Further Reading 	512
7.4.6 Exercises 	513
CHAPTER 8 Perception 	 	514

8.1 Image Processing Operators and Algorithms 	514
8.1.1 Taxonomy of Computer Vision Algorithms 	515
8.1.2 High Pass Filtering Operators 	517
8.1.3 Low Pass Operators 	523
8.1.4 Matching Signals and Images 	524
8.1.5 Feature Detection 	526
8.1.6 Region Processing 	529
8.1.7 References and Further Reading 	532
8.1.8 Exercises 	533
8.2 Physics and Principles of Radiative Sensors 	534
8.2.1 Radiative Sensors 	534
8.2.2 Techniques for Range Sensing 	535
8.2.3 Radiation 	539
8.2.4 Lenses, Filters and Mirrors 	545
8.2.5 References and Further Reading 	550
8.2.6 Exercises 	551
8.3 Sensors for Perception 	551
8.3.1 Laser Rangefinders 	551
8.3.2 Ultrasonic Rangefinders 	555
8.3.3 Visible Wavelength Cameras 	557
8.3.4 Mid to Far Infrared Wavelength Cameras 	560
8.3.5 Radars 	562
8.3.6 References and Further Reading 	564
8.3.7 Exercises 	565
8.4 Aspects of Geometric and Semantic Computer Vision 	565
8.4.1 Pixel Classification 	565
8.4.2 Computational Stereo Vision 	568
8.4.3 Obstacle Detection 	572
8.4.4 References and Further Reading 	576
8.4.5 Exercises 	576
CHAPTER 9 Localization and Mapping 	 	579

9.1 Representation and Issues 	580
9.1.1 Introduction 	580
9.1.2 Representation 	580
9.1.3 Timing and Motion Issues 	583
9.1.4 Related Localization Issues 	585
9.1.5 Structural Aspects 	586
9.1.6 Example: Unmanned Ground Vehicle (UGV) Terrain Mapping 	588
9.1.7 References and Further Reading 	592
9.1.8 Exercises 	593
9.2 Visual Localization and Motion Estimation 	593
9.2.1 Introduction 	593
9.2.2 Aligning Signals for Localization and Motion Estimation 	600
9.2.3 Matching Features for Localization and Motion Estimation 	606
9.2.4 Searching for the Optimal Pose 	612
9.2.5 References and Further Reading 	621
9.2.6 Exercises 	622
9.3 Simultaneous Localization and Mapping 	623
9.3.1 Introduction 	623
9.3.2 Global Consistency in Cyclic Maps 	624
9.3.3 Revisiting 	630
9.3.4 EKF SLAM For Discrete Landmarks 	632
9.3.5 Example: Autosurveying of Laser Reflectors 	636
9.3.6 References and Further Reading 	638
9.3.7 Exercises 	639
CHAPTER 10 Motion Planning 	 	640

10.1 Introduction 	640
10.1.1 Introducing Path Planning 	641
10.1.2 Formulation of Path Planning 	642
10.1.3 Obstacle Free Motion Planning 	643
10.1.4 References and Further Reading 	646
10.1.5 Exercises 	646
10.2 Representation and Search for Global Path Planning 	646
10.2.1 Sequential Motion Planning 	647
10.2.2 Big Ideas in Optimization and Search 	653
10.2.3 Uniform Cost Sequential Planning Algorithms 	656
10.2.4 Weighted Sequential Planning 	661
10.2.5 Representation for Sequential Motion Planning 	669
10.2.6 References and Further Reading 	672
10.2.7 Exercises 	672
10.3 Real Time Global Motion Planning 	673
10.3.1 Introduction 	673
10.3.2 Depth-Limited Approaches 	674
10.3.3 Anytime Approaches 	677
10.3.4 Plan Repair Approach: D* Algorithm 	678
10.3.5 Hierarchical Planning 	686
10.3.6 References and Further Reading 	689
10.3.7 Exercises 	690
Index 	 	691




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