Wheeled mobile robotics from fundamentals towards autonomous systems
Wheeled Mobile Robotics: From Fundamentals Towards Autonomous Systems covers the main topics from the wide area of mobile robotics, explaining all applied theory and application. The book gives the reader a good foundation, enabling them to continue to more advanced topics. Several examples are incl...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, England :
Butterworth-Heinemann
2017.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630135106719 |
Tabla de Contenidos:
- Front Cover
- Wheeled Mobile Robotics: From Fundamentals Towards Autonomous Systems
- Copyright
- Contents
- Preface
- Acknowledgements
- Chapter 1: Introduction to Mobile Robotics
- 1.1 Introduction
- 1.1.1 Robots
- 1.1.2 Mobile
- 1.1.3 Wheels
- 1.1.4 Autonomous Mobile Systems
- 1.2 History
- 1.3 About the Book
- References
- Chapter 2: Motion Modeling for Mobile Robots
- 2.1 Introduction
- 2.2 Kinematics of Wheeled Mobile Robots
- 2.2.1 Differential Drive
- 2.2.2 Bicycle Drive
- 2.2.3 Tricycle Drive
- 2.2.4 Tricycle With a Trailer
- 2.2.5 Car (Ackermann) Drive
- 2.2.6 Synchronous Drive
- 2.2.7 Omnidirectional Drive
- 2.2.8 Tracked Drive
- 2.3 Motion Constraints
- 2.3.1 Holonomic Constraints
- 2.3.2 Nonholonomic Constraints
- 2.3.3 Integrability of Constraints
- 2.3.4 Vector Fields, Distribution, Lie Bracket
- 2.3.5 Controllability of Wheeled Mobile Robots
- 2.4 Dynamic Model of a Mobile System With Constraints
- 2.4.1 State-Space Representation of the Dynamic Model of a Mobile System With Constraints
- 2.4.2 Kinematic and Dynamic Model of Differential Drive Robot
- Kinematic Model and Constraints
- Dynamic Model
- References
- Chapter 3: Control of Wheeled Mobile Systems
- 3.1 Introduction
- 3.2 Control to Reference Pose
- 3.2.1 Orientation Control
- 3.2.2 Forward-Motion Control
- 3.2.3 Basic Approaches
- Control to Reference Position
- Control to Reference Pose Using an Intermediate Point
- Control to Reference Pose Using an Intermediate Direction
- Control on a Segmented Continuous Path Determined by a Line and a Circle Arc
- From Reference Pose Control to Reference Path Control
- 3.3 Trajectory Tracking Control
- 3.3.1 Trajectory Tracking Control Using Basic Approaches
- 3.3.2 Decomposing Control to Feedforward and Feedback Action
- 3.3.3 Feedback Linearization.
- 3.3.4 Development of the Kinematic Trajectory-Tracking Error Model
- 3.3.5 Linear Controller
- 3.3.6 Lyapunov-Based Control Design
- 3.3.7 Takagi-Sugeno Fuzzy Control Design in the LMI Framework
- Takagi-Sugeno Fuzzy Error Model of a Differentially Driven Wheeled Mobile Robot
- PDC Control of a Differentially Driven Wheeled Mobile Robot
- 3.3.8 Model-Based Predictive Control
- Discrete MPC
- 3.3.9 Particle Swarm Optimization-Based Control
- MPC With PSO
- 3.3.10 Visual Servoing Approaches for Solving the Control Problems in Mobile Robotics
- 3.4 Optimal Velocity Profile Estimation for a Known Path
- References
- Chapter 4: Path Planning
- 4.1 Introduction
- 4.1.1 Robot Environment
- 4.1.2 Path Planning
- 4.1.3 Configuration and Configuration Space
- 4.1.4 Mathematical Description of Obstacle Shape and Pose in the Environment
- Border-Based Description of Obstacles
- Obstacle Description Using Intersecting Half-Planes
- 4.2 Environment Presentation for Path Planning Purposes
- 4.2.1 Graph Representation
- 4.2.2 Cell Decomposition
- Accurate Decomposition to Cells
- Approximate Decomposition to Cells
- 4.2.3 Roadmap
- Visibility Graph
- Voronoi Graph
- Triangulation
- 4.2.4 Potential Field
- 4.2.5 Sampling-Based Path-Planning
- RRT Method
- PRM Method
- 4.3 Simple Path Planning Algorithms:Bug Algorithms
- 4.3.1 Bug0 Algorithm
- 4.3.2 Bug1 Algorithm
- 4.3.3 Bug2 Algorithm
- 4.4 Graph-Based Path Planning Methods
- 4.4.1 Breadth-First Search
- 4.4.2 Depth-First Search
- 4.4.3 Iterative Deepening Depth-First Search
- 4.4.4 Dijkstra's Algorithm
- 4.4.5 A∗ Algorithm
- 4.4.6 Greedy Best-First Search
- References
- Chapter 5: Sensors Used in Mobile Systems
- 5.1 Introduction
- 5.2 Coordinate Frame Transformations
- 5.2.1 Orientation and Rotation
- 5.2.2 Translation and Rotation.
- 5.2.3 Kinematics of Rotating Frames
- Rotational Kinematics Expressed by Quaternions
- 5.2.4 Projective Geometry
- Multiview Geometry
- Singular Cases
- 3D Reconstruction
- 5.3 Pose Measurement Methods
- 5.3.1 Dead Reckoning
- Odometry
- Inertial Navigation
- 5.3.2 Heading Measurement
- 5.3.3 Active Markers and Global Position Measurement
- Global Navigation Satellite System
- 5.3.4 Navigation Using Environmental Features
- Straight-Line Features
- Split-and-Merge Algorithm
- Evolving Straight-Line Clustering
- Hough Transform
- Color Features
- Artificial Pattern Markers
- Natural Local Image Features
- 5.3.5 Matching of Environment Models: Maps
- 5.4 Sensors
- 5.4.1 Sensor Characteristics
- 5.4.2 Sensor Classifications
- References
- Chapter 6: Nondeterministic Events in Mobile Systems
- 6.1 Introduction
- 6.2 Basics of Probability
- 6.2.1 Discrete Random Variable
- 6.2.2 Continuous Random Variable
- 6.2.3 Bayes' Rule
- 6.3 State Estimation
- 6.3.1 Disturbances and Noise
- 6.3.2 Estimate Convergence and Bias
- 6.3.3 Observability
- 6.4 Bayesian Filter
- 6.4.1 Markov Chains
- 6.4.2 State Estimation From Observations
- 6.4.3 State Estimation From Observations and Actions
- 6.4.4 Localization Example
- 6.4.5 Environment Sensing
- 6.4.6 Motion in the Environment
- 6.4.7 Localization in the Environment
- 6.5 Kalman Filter
- 6.5.1 Kalman Filter in Matrix Form
- 6.5.2 Extended Kalman Filter
- 6.5.3 Kalman Filter Derivatives
- 6.6 Particle Filter
- References
- Chapter 7: Autonomous Guided Vehicles
- 7.1 Introduction
- 7.2 Autonomous Transportation Vehicles
- 7.2.1 About
- 7.2.2 Setup
- 7.2.3 Sensors
- 7.2.4 Localization and Mapping
- 7.2.5 Control
- 7.2.6 Path Planning
- 7.2.7 Decision Making
- 7.3 Wheeled Mobile Robots in Agriculture
- 7.3.1 Introduction (About)
- 7.3.2 Service Unit Setup.
- 7.3.3 Localization, Mapping, and SLAM
- 7.3.4 Control Strategies
- 7.3.5 Planning Routes and Scheduling
- 7.4 Wheeled Mobile Robots in Industry
- 7.4.1 About
- 7.4.2 Setup
- 7.4.3 Sensors
- 7.4.4 Localization and Mapping
- 7.4.5 Control
- 7.4.6 Path Planning
- 7.4.7 Decision Making
- 7.5 Wheeled Mobile Robots in Domestic Environments
- 7.5.1 About
- 7.5.2 Setup
- 7.5.3 Sensors
- 7.5.4 Localization and Mapping
- 7.5.5 Path Planning
- 7.5.6 Control
- 7.5.7 Decision Making
- 7.6 Assistive Mobile Robots in Walking Rehabilitation Therapy
- 7.6.1 About
- 7.6.2 Setup
- 7.6.3 Sensors
- 7.6.4 Localization and Mapping
- 7.6.5 Control
- 7.6.6 Path Planning
- References
- Chapter 8: Project Examples for Laboratory Practice
- 8.1 Introduction
- 8.2 Localization Based on Bayesian Filter Within a Network of Paths and Crossroads
- 8.2.1 Introduction
- 8.2.2 Line Following
- 8.2.3 Odometry
- 8.2.4 Map Building
- 8.2.5 Localization
- 8.3 Localization Based on Extended Kalman Filter Using Indoor Global Positioning System and Odometry
- 8.3.1 Introduction
- 8.3.2 Experimental Setup
- 8.3.3 Extended Kalman Filter
- 8.4 Particle-Filter-Based Localization in a Pattern of Colored Tiles
- 8.4.1 Introduction
- 8.4.2 Experimental Setup
- 8.4.3 Manual Control
- 8.4.4 Wheel Odometry
- 8.4.5 Color Sensor Calibration
- 8.4.6 Particle Filter
- 8.5 Control of Vehicles in a Linear Formation
- 8.5.1 Introduction
- 8.5.2 Localization Using Odometry
- 8.5.3 Estimating Reference Trajectory
- 8.5.4 Linear Formation Control
- 8.6 Motion and Strategy Control of Multiagent Soccer Robots
- 8.6.1 Introduction
- 8.6.2 Motion Control
- 8.6.3 Behavior-Based Agent Operation
- 8.6.4 Multiagent Game Strategy
- 8.7 Image-Based Control of a Mobile Robot
- 8.7.1 Introduction
- 8.7.2 Experimental Setup
- 8.7.3 Position-Based Visual Servoing.
- 8.7.4 Image-Based Visual Servoing
- 8.7.5 Natural Image Features
- 8.8 Particle-Filter-Based Localization Using an Array of Ultrasonic Distance Sensors
- 8.8.1 Introduction
- 8.8.2 Predicting Particles' Locations From Known Robot Motion
- 8.8.3 Sensor Model for Particle Measurement Prediction
- 8.8.4 Correction Step of Particle Filter and Robot Pose Estimate
- 8.9 Path Planning of a Mobile Robot in a Known Environment
- 8.9.1 Experimental Setup
- 8.9.2 A* Path Searching Algorithm
- 8.9.3 Optimum Path in the Map of the Environment
- References
- Index
- Back Cover.
