Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms
- Length: 288 pages
- Edition: 1
- Language: English
- Publisher: The MIT Press
- Publication Date: 2022-12-20
- ISBN-10: 0262047551
- ISBN-13: 9780262047555
- Sales Rank: #608971 (See Top 100 Books)
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 classroomWritten for engineering and computer science undergraduates with a sophomore-level understanding of linear algebra, probability theory, trigonometry, and statisticsQR codes in the text guide readers to online lecture videos and animationsTopics 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 variantsExtensive appendices focus on project-based curricula, pertinent areas of mathematics, backpropagation, writing a research paper, and other topicsA growing library of exercises in an open-source, platform-independent simulation (Webots)
Title Page Copyright Dedication Contents Preface 1. Introduction 1.1. Intelligence and Embodiment 1.2. A Roboticists’ Problem 1.3. Ratslife: An Example of Autonomous Mobile Robotics 1.4. Autonomous Mobile Robots: Some Core Challenges 1.5. Autonomous Manipulation: Some Core Challenges I. Mechanisms 2. Locomotion, Manipulation, and Their Representations 2.1. Locomotion and Manipulation Examples 2.2. Static and Dynamic Stability 2.3. Degrees of Freedom 2.4. Coordinate Systems and Frames of Reference 2.4.1. Matrix Notation 2.4.2. Mapping from One Frame to Another 2.4.3. Concatenation of Transformations 2.4.4. Other Representations for Orientation 3. Kinematics 3.1. Forward Kinematics 3.1.1. Forward Kinematics of a Simple Robot Arm 3.1.2. The Denavit-Hartenberg Notation 3.2. Inverse Kinematics 3.2.1. Solvability 3.2.2. Inverse Kinematics of a Simple Manipulator Arm 3.3. Differential Kinematics 3.3.1. Forward Differential Kinematics 3.3.2. Forward Kinematics of a Differential-Wheel Robot 3.3.3. Forward Kinematics of Carlike Steering 3.4. Inverse Differential Kinematics 3.4.1. Inverse Kinematics of Mobile Robots 3.4.2. Feedback Control for Mobile Robots 3.4.3. Under-Actuation and Over-Actuation 4. Forces 4.1. Statics 4.2. Kineto-Statics Duality 4.3. Manipulability 4.3.1. Manipulability Ellipsoid in Velocity Space 4.3.2. Manipulability Ellipsoid in Force Space 4.3.3. Manipulability Considerations 5. Grasping 5.1. The Theory of Grasping 5.1.1. Friction 5.1.2. Multiple Contacts and Deformation 5.1.3. Suction 5.2. Simple Grasping Mechanisms 5.2.1. 1-DoF Scissorlike Gripper 5.2.2. Parallel Jaw 5.2.3. 4-Bar Linkage Parallel Gripper 5.2.4. Multifingered Hands II. Sensing and Actuation 6. Actuators 6.1. Electric Motors 6.1.1. AC and DC Motors 6.1.2. Stepper Motor 6.1.3. Brushless DC Motor 6.1.4. Servo Motor 6.1.5. Motor Controllers 6.2. Hydraulic and Pneumatic Actuators 6.2.1. Hydraulic Actuators 6.2.2. Pneumatic Actuators and Soft Robotics 6.3. Safety Considerations 7. Sensors 7.1. Terminology 7.1.1. Proprioception versus Exteroception 7.2. Sensors That Measure the Robot’s Joint Configuration 7.3. Sensors That Measure Ego-Motion 7.3.1. Accelerometers 7.3.2. Gyroscopes 7.4. Measuring Force 7.4.1. Measuring Pressure or Touch 7.5. Sensors to Measure Distance 7.5.1. Reflection 7.5.2. Phase Shift 7.5.3. Time of Flight 7.6. Sensors to Sense Global Pose III. Computation 8. Vision 8.1. Images as Two-Dimensional Signals 8.2. From Signals to Information 8.3. Basic Image Operations 8.3.1. Threshold-Based Operations 8.3.2. Convolution-Based Filters 8.3.3. Morphological Operations 8.4. Extracting Structure from Vision 8.5. Computer Vision and Machine Learning 9. Feature Extraction 9.1. Feature Detection as an Information-Reduction Problem 9.2. Features 9.3. Line Recognition 9.3.1. Line Fitting Using Least Squares 9.3.2. Split-and-Merge Algorithm 9.3.3. RANSAC: Random Sample and Consensus 9.3.4. The Hough Transform 9.4. Scale-Invariant Feature Transforms 9.4.1. Overview 9.4.2. Object Recognition Using Scale-Invariant Features 9.5. Feature Detection and Machine Learning 10. Artificial Neural Networks 10.1. The Simple Perceptron 10.1.1. Geometric Interpretation of the Simple Perceptron 10.1.2. Training the Simple Perceptron 10.2. Activation Functions 10.3. From the Simple Perceptron to Multilayer Neural Networks 10.3.1. Formal Description of Artificial Neural Networks 10.3.2. Training a Multilayer Neural Network 10.4. From Single Outputs to Higher Dimensional Data 10.5. Objective Functions and Optimization 10.5.1. Loss Functions for Regression Tasks 10.5.2. Loss Functions for Classification Tasks 10.5.3. Binary and Categorical Cross-Entropy 10.6. Convolutional Neural Networks 10.6.1. From Convolutions to 2D Neural Networks 10.6.2. Padding and Striding 10.6.3. Pooling 10.6.4. Flattening 10.6.5. A Sample CNN 10.6.6. Convolutional Networks beyond 2D Image Data 10.7. Recurrent Neural Networks 11. Task Execution 11.1. Reactive Control 11.1.1. Limitations of Reactive Control 11.2. Finite State Machines 11.2.1. Implementation 11.3. Hierarchical Finite State Machines 11.3.1. Implementation 11.4. Behavior Trees 11.4.1. Node Definition and Status 11.4.2. Node Types 11.4.3. Behavior Tree Execution 11.4.4. Implementation 11.5. Mission Planning 11.5.1. The General Problem Solver and STRIPS 12. Mapping 12.1. Map Representations 12.2. Iterative Closest Point for Sparse Mapping 12.3. Octomap: Dense Mapping of Voxels 12.4. RGB-D Mapping: Dense Mapping of Surfaces 13. Path Planning 13.1. The Configuration Space 13.2. Graph-Based Planning Algorithms 13.2.1. Dijkstra’s Algorithm 13.2.2. A* 13.3. Sampling-Based Path Planning 13.3.1. Rapidly Exploring Random Trees 13.4. Planning at Different Length Scales 13.5. Coverage Path Planning 13.6. Summary and Outlook 14. Manipulation 14.1. Nonprehensile Manipulation 14.2. Choosing the Right Grasp 14.2.1. Finding Good Grasps for Simple Grippers 14.2.2. Finding Good Grasps for Multifingered Hands 14.3. Pick and Place 14.4. Peg-in-Hole Problems IV. Uncertainty 15. Uncertainty and Error Propagation 15.1. Uncertainty in Robotics as a Random Variable 15.2. Error Propagation 15.2.1. Example: Line Fitting 15.2.2. Example: Odometry 15.3. Optimal Sensor Fusion 15.3.1. The Kalman Filter 16. Localization 16.1. Motivating Example 16.2. Markov Localization 16.2.1. Perception Update 16.2.2. Action Update 16.2.3. Example: Markov Localization on a Topological Map 16.3. The Bayes Filter 16.3.1. Example: Bayes Filter on a Grid 16.4. Particle Filter 16.5. Extended Kalman Filter 16.5.1. Odometry Using the Kalman Filter 16.6. Summary: Probabilistic Map-Based Localization 17. Simultaneous Localization and Mapping 17.1. Introduction 17.1.1. Landmarks 17.1.2. Special Case I: One Landmark 17.1.3. Special Case II: Two Landmarks 17.2. The Covariance Matrix 17.3. EKF SLAM 17.3.1. Algorithm 17.3.2. Multiple Sensors 17.4. Graph-Based SLAM 17.4.1. SLAM as a Maximum-Likelihood Estimation Problem 17.4.2. Numerical Techniques for Graph-Based SLAM V. Appendixes A. Trigonometry A.1. Inverse Trigonometry A.2. Trigonometric Identities B. Linear Algebra B.1. Dot Product B.2. Cross Product B.3. Matrix Product B.4. Matrix Inversion B.5. Principal Component Analysis C. Statistics C.1. Random Variables and Probability Distributions C.1.1. The Normal Distribution C.1.2. Normal Distribution in Two Dimensions C.2. Conditional Probabilities and Bayes’ Rule C.3. Sum of Two Random Processes C.4. Linear Combinations of Independent Gaussian Random Variables C.5. Testing Statistical Significance C.5.1. Null Hypothesis on Distributions C.5.2. Testing Whether Two Distributions Are Independent C.5.3. Statistical Significance of True-False Tests C.5.4. Summary D. Backpropagation D.1. Backward Propagation of Error D.2. Backpropagation Algorithm E. How to Write a Research Paper E.1. Original Research E.2. Hypothesis: Or, What Do We Learn from This Work? E.3. Survey and Tutorial E.4. Writing It Up! F. Sample Curricula F.1. An Introduction to Autonomous Mobile Robots F.1.1. Overview F.1.2. Content F.1.3. Implementation Suggestions F.2. An Introduction to Robotic Manipulation F.2.1. Overview F.2.2. Content F.2.3. Implementation Suggestions F.3. An Introduction to Robotic Systems F.3.1. Overview F.3.2. Content F.3.3. Implementation Suggestions F.4. Class Debates References Index
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