Nguồn ngoài
Introduction to Autonomous Robots
This book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping.
- Tác giả
- Nikolaus Correll
- Nhà xuất bản
- University of Colorado at Boulder
- Nguồn
- LibreTexts
- Giấy phép
- CC BY-NC
- Ngôn ngữ
- EN
Mục lục· 97 mục
- 01: Introduction
- 1.01: Intelligence and Embodiment
- 1.02: A Roboticists Problem
- 1.03: Ratslife
- 1.04: Challenges of Mobile Autonomous Robots
- 1.05: Challenges of Autonomous Manipulation
- 1.06: Exercises
- 02: Locomotion and Manipulation
- 2.01: Locomotion and Manipulation Examples
- 2.02: Static and Dynamic Stability
- 2.03: Degrees-of-Freedom
- 2.04: Exercises
- 03: Forward and Inverse Kinematics
- 3.01: Coordinate Systems and Frames of Reference
- 3.02: Forward kinematics of selected Mechanisms
- 3.03: Forward Kinematics using the Denavit-Hartenberg scheme
- 3.04: Inverse Kinematics of Selected Mechanisms
- 3.05: Inverse Kinematics using Feedback-Control
- 3.06: Exercises
- 04: Path Planning
- 4.01: Map Representations
- 4.02: Path-Planning Algorithms
- 4.03: Sampling-based Path Planning
- 4.04: Path Smoothing
- 4.05: Planning at different length-scales
- 4.06: Other Path-Planning Applications
- 4.07: Exercises
- 05: Sensors
- 5.01: Robotic Sensors
- 5.02: Proprioception of Robot Kinematics and Internal forces
- 5.03: Sensors Using Light
- 5.04: Sensors using Sound
- 5.05: Inertia-based Sensors
- 5.06: Beacon-based Sensors
- 5.07: Terminology
- 5.08: Exercises
- 06: Vision
- 6.01: Images as Two-Dimensional Signals
- 6.02: From Signals to Information
- 6.03: Basic Image Operations
- 6.04: Exercises
- 07: Feature Extraction
- 7.01: Feature Detection as an Information-Reduction Problem
- 7.02: Features
- 7.03: Line Recognition
- 7.04: Scale-Invariant Feature Transforms
- 7.05: Exercises
- 08: Uncertainty and Error Propagation
- 8.01: Uncertainty in Robotics as Random Variable
- 8.02: Error Propogation
- 8.03: Exercises
- 09: Localization
- 9.01: Motivating Example
- 9.02: Markov Localization
- 9.03: Particle Filter
- 9.04: The Kalman Filter
- 9.05: Extended Kalman Filter
- 9.06: Exercises
- 10: Grasping
- 10.01: The Theory of Grasping
- 10.02: Simple Grasping Mechanisms
- 10.03: How to Find Good Grasps
- 10.04: Manipulation
- 10.05: Exercises
- 11: Simultaneous Localization and Mapping
- 11.01: Introduction
- 11.02: The Covariance Matrix
- 11.03: EKF SLAM
- 11.04: Graph-based SLAM
- 12: RGB-D SLAM
- 12.01: Converting Range Data into Point Cloud Data
- 12.02: The Iterative Closest Point (ICP) Algorithm
- 12.03: RGB-D Mapping
- 13: Trigonometry
- 13.01: Inverse trigonometry
- 13.02: Trigonometric Identities
- 14: Linear Algebra
- 14.01: Dot Product
- 14.02: Cross Product
- 14.03: Matrix Product
- 14.04: Matrix Inversion
- 14.05: Principal Component Analysis
- 15: Statistics
- 15.01: Random Variables and Probability Distributions
- 15.02: Conditional Probabilities and Bayes Rule
- 15.03: Sum of Two Random Processes
- 15.04: Linear Combinations of Independent Gaussian Random Variables
- 15.05: Testing Statistical Significance
- 16: How to Write a Research Paper
- 16.01: Original
- 16.02: Hypothesis- Or What Do We Learn From this Work
- 16.03: Survey and Tutorial
- 16.04: Writing it up
- 17: Sample Curricula
- 17.01: An Introduction to Autonomous Mobile Robots
- 17.02: An Introduction to Autonomous Manipulation
- 17.03: Class Debates