Course

PROFESSIONAL ELECTIVES

Electives in Artificial Intelligence

Unit I

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

Unit II

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 III

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

Course Objectives

• 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.

Course Outcomes

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

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

Textbook(s)

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

Reference(s)

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.