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Introduction - Introduction to Robotics.
Lecture 1.1 - Introduction.
Lecture - 1.2 - Evolution of Robotics.
Lecture - 2 .1 - Kinematics- Coordinate transformations.
Lecture - 2.2 - Homogeneus Transformation Matrix.
Lecture - 2.3 - Industrial Robot- Kinematic Structures.
Lecture - 2.4 - Robot Architectures.
Lecture - 2.5 - Kinematic Parameters.
Lecture - 2.6 - DH Algorithm.
Lecture - 2.7 - DH Algorithm.
Lecture - 2.8 - Forward Kinematics.
Lecture - 2.9 - Forward Kinematics- Examples.
Lecture - 2.10 -Inverse Kinematics.
Lecture - 2.11 - Inverse Kinematics- Examples.
Lecture - 2.12 - Differential Relations.
Lecture - 2.13 - Manipulator Jacobian and Statics.
Lecture - 3.1 Overview of Electric Actuators and Operational Needs.
Lecture 3.2 - Principles of DC Motor Operation.
Lecture 3.3 - DC Motor Equations and Principles of Control.
Lecture 4.1 - DC Motor Control Regions and Principles of Power Electronics.
Lecture 4.2 - Power Electronic Switching and Current Ripple.
Lecture 4.3 - The H-Bridge and DC Motor Control Structure.
Lecture 5.1 - The Brushless DC Machine.
Lecture 5.2 - Control of the Brushless DC Motor.
Lecture 5.3 - The PM Synchronous Motor (PMSM) and SPWM.
Lecture 6.1 - Principles of PMSM Control.
Lecture 6.2 - Encoders for Speed and Position Estimation.
Lecture 6.3 - Stepper Motors.
Lecture 7.1 - Introduction to Probabilistic Robotics..
Lecture 7.2 - Recursive State Estimation: Bayes Filter.
Lecture 7.3 - Recursive State Estimation: Bayes Filter Illustration..
Tutorial - 1 Probability Basics.
Tutorial - 2 Probability Basics.
Lecture 8.1 - Kalman Filter.
Lecture 8.2 - Extended Kalman Filter.
Lecture 8.3 - Particle Filter.
Lecture 8.4 - Binary Bayes.
Lecture - 9.1 Velocity Motion Model.
Lecture - 9.2 Odometry Motion Model.
Lecture - 9.3 Occupa Grid Mapping.
Lecture 9.4 - Range Finder Measurement Model.
Lecture 10.1 - Localization Taxonomy.
Lecture 10.2 - Markov Localization.Lecture 10.3 - Path Planning.
COURSE OUTLINE: This course is a bridge course for students from various disciplines to get the basic understanding of robotics. The mechanical, electrical, and computer science aspects of robotics is covered in this introductory course.
ABOUT INSTRUCTOR: Dr. T Asokan is a Professor in the Department of Engineering Design, and currently the Head of the Department, at IIT Madras. has more than 25 years of professional experience in research and teaching in the broad areas of Robotics, Product design, and Engineering System design. Dr Asokan has published more than 100 papers in International Journals and conferences and has filed 18 patents in India, USA, and Singapore. More details can be found at https://ed.iitm.ac.in/~asokan/ Prof Krishna Vasudevan is a professor in the department of electrical engineering at IIT Madras, with more than 25 years of professional experience. His area of specialization is drives and controls. Prof. Balaraman Ravindran is currently an Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow . He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.
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