Course curriculum

  • 1

    Robotics Lecture 01 - Introduction to Robotics

    • Robotics Lecture 01 - Introduction to Robotics

    • Lecture 02 - Introduction to manipulator kinematics

    • Robotics Lecture 03 - Spatial Descriptions

    • Robotics Lecture 04 - Rotations

    • Robotics Lecture 05 - Rigid Body Transformations

    • Robotics Lecture 06 - Forward Kinematics

    • Robotics Lecture 07 - Denavit-Hartenberg Examples

    • Robotics Lecture 08 - Inverse Kinematics

    • Robotics Lecture 09 - Inverse Kinematics and Trajectory Generation

    • Robotics Lecture 10 - Kinematics of Wheeled Robots

    • Robotics Lecture 11 - Mobile Robot Forward Kinematics

    • Robotics Lecture 12 - Probability Review

    • Robotics Lecture 13 - Conditional Probability

    • Robotics Lecture 14 - Velocity Motion Model

    • Robotics Lecture 15 - Motion Models

    • Robotics Lecture 16 - Motion Models II

    • Robotics Lecture 17 - Combining Noisy Measurements

    • Robotics Lecture 18 - Bayes and Kalman Filter

    • Robotics Lecture 19 - Bayes and Kalman Filter Examples

    • Robotics Lecture 20 - Extended Kalman Filter

    • Robotics Lecture 21 - EKF and UKF

    • Robotics Lecture 22 - Non-parametric Filters

    • Robotics Lecture 23 - Non-parametric Filters_ Particle Filters

    • Robotics Lecture 24 - Bug Algorithms

    • Robotics Lecture 25 - Path Planning in Discrete Sampled Space

    • Robotics Lecture 26 - Simultaneous Localization and Mapping

    • Robotics Lecture 27 - Range Sensor Models

    • Robotics Lecture 28 - Range Sensor Models II

    • Robotics Lecture 29 - Case Study