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Introduction to Autonomous Robots

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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.

Mechanical EngineeringEngineering
Author
Nikolaus Correll
Publisher
University of Colorado at Boulder
Source
LibreTexts
License
CC BY-NC
Language
EN
Read online Download PDF (open licence)
Contents· 97 sections
  1. 01: Introduction
  2. 1.01: Intelligence and Embodiment
  3. 1.02: A Roboticists Problem
  4. 1.03: Ratslife
  5. 1.04: Challenges of Mobile Autonomous Robots
  6. 1.05: Challenges of Autonomous Manipulation
  7. 1.06: Exercises
  8. 02: Locomotion and Manipulation
  9. 2.01: Locomotion and Manipulation Examples
  10. 2.02: Static and Dynamic Stability
  11. 2.03: Degrees-of-Freedom
  12. 2.04: Exercises
  13. 03: Forward and Inverse Kinematics
  14. 3.01: Coordinate Systems and Frames of Reference
  15. 3.02: Forward kinematics of selected Mechanisms
  16. 3.03: Forward Kinematics using the Denavit-Hartenberg scheme
  17. 3.04: Inverse Kinematics of Selected Mechanisms
  18. 3.05: Inverse Kinematics using Feedback-Control
  19. 3.06: Exercises
  20. 04: Path Planning
  21. 4.01: Map Representations
  22. 4.02: Path-Planning Algorithms
  23. 4.03: Sampling-based Path Planning
  24. 4.04: Path Smoothing
  25. 4.05: Planning at different length-scales
  26. 4.06: Other Path-Planning Applications
  27. 4.07: Exercises
  28. 05: Sensors
  29. 5.01: Robotic Sensors
  30. 5.02: Proprioception of Robot Kinematics and Internal forces
  31. 5.03: Sensors Using Light
  32. 5.04: Sensors using Sound
  33. 5.05: Inertia-based Sensors
  34. 5.06: Beacon-based Sensors
  35. 5.07: Terminology
  36. 5.08: Exercises
  37. 06: Vision
  38. 6.01: Images as Two-Dimensional Signals
  39. 6.02: From Signals to Information
  40. 6.03: Basic Image Operations
  41. 6.04: Exercises
  42. 07: Feature Extraction
  43. 7.01: Feature Detection as an Information-Reduction Problem
  44. 7.02: Features
  45. 7.03: Line Recognition
  46. 7.04: Scale-Invariant Feature Transforms
  47. 7.05: Exercises
  48. 08: Uncertainty and Error Propagation
  49. 8.01: Uncertainty in Robotics as Random Variable
  50. 8.02: Error Propogation
  51. 8.03: Exercises
  52. 09: Localization
  53. 9.01: Motivating Example
  54. 9.02: Markov Localization
  55. 9.03: Particle Filter
  56. 9.04: The Kalman Filter
  57. 9.05: Extended Kalman Filter
  58. 9.06: Exercises
  59. 10: Grasping
  60. 10.01: The Theory of Grasping
  61. 10.02: Simple Grasping Mechanisms
  62. 10.03: How to Find Good Grasps
  63. 10.04: Manipulation
  64. 10.05: Exercises
  65. 11: Simultaneous Localization and Mapping
  66. 11.01: Introduction
  67. 11.02: The Covariance Matrix
  68. 11.03: EKF SLAM
  69. 11.04: Graph-based SLAM
  70. 12: RGB-D SLAM
  71. 12.01: Converting Range Data into Point Cloud Data
  72. 12.02: The Iterative Closest Point (ICP) Algorithm
  73. 12.03: RGB-D Mapping
  74. 13: Trigonometry
  75. 13.01: Inverse trigonometry
  76. 13.02: Trigonometric Identities
  77. 14: Linear Algebra
  78. 14.01: Dot Product
  79. 14.02: Cross Product
  80. 14.03: Matrix Product
  81. 14.04: Matrix Inversion
  82. 14.05: Principal Component Analysis
  83. 15: Statistics
  84. 15.01: Random Variables and Probability Distributions
  85. 15.02: Conditional Probabilities and Bayes Rule
  86. 15.03: Sum of Two Random Processes
  87. 15.04: Linear Combinations of Independent Gaussian Random Variables
  88. 15.05: Testing Statistical Significance
  89. 16: How to Write a Research Paper
  90. 16.01: Original
  91. 16.02: Hypothesis- Or What Do We Learn From this Work
  92. 16.03: Survey and Tutorial
  93. 16.04: Writing it up
  94. 17: Sample Curricula
  95. 17.01: An Introduction to Autonomous Mobile Robots
  96. 17.02: An Introduction to Autonomous Manipulation
  97. 17.03: Class Debates