Introduction to Robotics

Introduction to Robotics

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Introduction - Introduction to Robotics

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Introduction - Introduction to Robotics

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Introduction to Robotics

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  1. 1 Introduction - Introduction to Robotics
  2. 2 Lecture 1.1 - Introduction
  3. 3 Lecture - 1.2 - Evolution of Robotics
  4. 4 Lecture - 2 .1 - Kinematics- Coordinate transformations
  5. 5 Lecture - 2.2 - Homogeneus Transformation Matrix
  6. 6 Lecture - 2.3 - Industrial Robot- Kinematic Structures
  7. 7 Lecture - 2.4 - Robot Architectures
  8. 8 Lecture - 2.5 - Kinematic Parameters
  9. 9 Lecture - 2.6 - DH Algorithm
  10. 10 Lecture - 2.7 - DH Algorithm
  11. 11 Lecture - 2.8 - Forward Kinematics
  12. 12 Lecture - 2.9 - Forward Kinematics- Examples
  13. 13 Lecture - 2.10 -Inverse Kinematics
  14. 14 Lecture - 2.11 - Inverse Kinematics- Examples
  15. 15 Lecture - 2.12 - Differential Relations
  16. 16 Lecture - 2.13 - Manipulator Jacobian and Statics
  17. 17 Lecture - 3.1 Overview of Electric Actuators and Operational Needs
  18. 18 Lecture 3.2 - Principles of DC Motor Operation
  19. 19 Lecture 3.3 - DC Motor Equations and Principles of Control
  20. 20 Lecture 4.1 - DC Motor Control Regions and Principles of Power Electronics
  21. 21 Lecture 4.2 - Power Electronic Switching and Current Ripple
  22. 22 Lecture 4.3 - The H-Bridge and DC Motor Control Structure
  23. 23 Lecture 5.1 - The Brushless DC Machine
  24. 24 Lecture 5.2 - Control of the Brushless DC Motor
  25. 25 Lecture 5.3 - The PM Synchronous Motor (PMSM) and SPWM
  26. 26 Lecture 6.1 - Principles of PMSM Control
  27. 27 Lecture 6.2 - Encoders for Speed and Position Estimation
  28. 28 Lecture 6.3 - Stepper Motors
  29. 29 Lecture 7.1 - Introduction to Probabilistic Robotics.
  30. 30 Lecture 7.2 - Recursive State Estimation: Bayes Filter
  31. 31 Lecture 7.3 - Recursive State Estimation: Bayes Filter Illustration.
  32. 32 Tutorial - 1 Probability Basics
  33. 33 Tutorial - 2 Probability Basics
  34. 34 Lecture 8.1 - Kalman Filter
  35. 35 Lecture 8.2 - Extended Kalman Filter
  36. 36 Lecture 8.3 - Particle Filter
  37. 37 Lecture 8.4 - Binary Bayes
  38. 38 Lecture - 9.1 Velocity Motion Model
  39. 39 Lecture - 9.2 Odometry Motion Model
  40. 40 Lecture - 9.3 Occupa Grid Mapping
  41. 41 Lecture 9.4 - Range Finder Measurement Model
  42. 42 Lecture 10.1 - Localization Taxonomy
  43. 43 Lecture 10.2 - Markov Localization
  44. 44 Lecture 10.3 - Path Planning

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