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Course Detail

Course Name Artificial Intelligence And Robotics
Course Code 25CSC432
Program 5 Year Integrated M.Sc in Data Science, Integrated M. Sc. Mathematics and Computing
Credits 3
Campus Coimbatore

Syllabus

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.

Objectives and Outcomes

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

 PO/PSO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

CO

CO1

3

2

2

2

1

1

1

1

1

2

1

1

3

2

CO2

3

2

3

2

2

2

1

2

1

2

2

2

3

2

CO3

3

2

3

2

3

2

2

2

2

2

2

2

3

2

CO4

3

1

2

3

3

2

2

2

2

2

2

2

3

2

CO5

3

1

2

2

3

1

2

2

2

2

2

2

3

2

Evaluation Pattern

Evaluation Pattern: 70:30

Assessment

Internal

End Semester

Midterm

20

 

Continuous Assessment – Theory (*CAT)

10

 

Continuous Assessment – Lab (*CAL)

40

 

**End Semester exam

 

30 (50 Marks; 2 hours exam)

 

*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

Text Books / References

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.

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