Course

Unit 1

Introduction, Actuators and drives, Control components, De-mining Robot: Embedded Robot Controller, I/O Interface, and PWM Amplifiers, control software, sensor inputs, sensors.

Unit 2

Kinematics, differential motion, statics, energy method, hybrid position force control, Non-holonomic systems, dynamics – Translational and Rotational, computed torque control, Transformation, Path Planning, and Trajectories, Time Response of Dynamic Systems, Dynamic Effects of Feedback Control, Control Systems – Artificial Intelligence based optimal control, Applications of Machine Learning and Deep learning in robot navigation.

Unit 3

Numerical Optimization, Dynamic Optimal Control, Parameter Estimation and Adaptive Control, Application of Computer vision in robotics, Tele-robotics and virtual reality.

• This course aims to make the students understand the basic principles in AI and robotics technologies.
• The students will be able to apply machine learning algorithms for applications using AI and robotics.

• CO1: Understand the fundamentals of robots and their components.
• CO2: Design and develop kinematic operation for a robotic manipulator.
• CO3: Understand different algorithms for path planning and navigation.
• CO4: Apply AI and Robotics technologies using basic programming and machine learning.
• CO5: Understand societal and business impact of AI and Robotics technologies.

CO-PO Mapping

• Asada H, Slotine JJ. “Robot analysis and control”. John Wiley & Sons; 1986.

• Iosifidis, Alexandros, and Anastasios Tefas, eds. “Deep Learning for Robot Perception and Cognition”. Academic Press, 2022.
• Yoshikawa, Tsuneo. “Foundations of robotics: analysis and control”. MIT press, 2003.
• Spong MW. Seth Hutchinson and Mathukumalli Vidyasagar. “In Robot modeling and control”; 2020.
• Lynch KM, Park FC. “Modern Robotics”. First Edition, Cambridge University Press, 2017.
• John JC. “Introduction to robotics: mechanics and control”. Third Edition, Pearson publication, 2004.
• Kelly A. “Mobile robotics: mathematics, models, and methods”. Cambridge University Press; 2013.
• Thrun S, Burgard W, Fox D. “Probabilistic robotics”. MIT press; 2005.
• Siciliano B, Khatib O. “Handbook of robotics. Section kinematic loops”;2008.
• Richard S. Sutton, Andrew G. Barto, Francis Bach, “Reinforcement Learning: An Introduction”, MIT Press, 2018.

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation