Semester Offering: August
Currently robots are extensively used in many industrial applications. Further the robotics has extended the horizons to bio medical, entertainment and elderly care applications in the recent past. Main objective of this course it to impart knowledge and experiences of robot design and analysis, to students. This course integrates the knowledge on control systems, kinematics and dynamics which students have studied in their undergraduate level to be applied for robot design, control and analysis.


Upon completion of this course, the students would be able to:
  • Select an appropriate robot for a given application based on the specifications.
  • Analyze a given robot design in terms of kinematics and dynamics.
  • Design and develop a robot to accomplish a specified task.
  • Apply the classical control theory for controlling and programming a robot.




I.             Machine Intelligence Technologies: Neural Networks
1.      Introduction to Neural Networks
2.      Perceptron Learning Rule
3.      Hebbian Learning
4.      Widrow-Hoff Learning
5.      Backpropagation
6.      Associative Learning
7.      Competitive Networks
8.      Grossberg Networks and Adaptive Resonance Theory
9.      Hopfield Networks

II.         Fuzzy Set Theory
1.      Introduction to Fuzzy Set with Properties
2.      Fuzzy Relations
3.      Fuzzy Arithmetic
4.      Fuzzy Logic
5.      Applications and Fuzzy Control
III.      Genetic Algorithm
1.      Introduction to Genetic Algorithm
2.      GA Operations
3.      Standard Method
4.      Rank Method
5.      Rank Space Method

IV.       Simulated Annealing
1.      Introduction to Annealing Process
2.      Simulated Annealing Optimization

V.          Particle Swarm Optimization
1.      Introduction to Swarm Behavior
2.      Particle Swarm Optimization

VI.       Artificial Intelligence
1.      Introduction to Artificial Intelligence
2.      Knowledge Representation
3.      Blind Search, Heuristic Search, and Optimal Search
4.      Adversarial Search


         Introduction to robot simulation software
         Drafting of rigid bodies and link mechanisms using Solidworks
         Matlab as a tool for Robot analysis and relevant tool boxes
         Forward Kinematics
         DH Parameters
         Velocity Kinematics
         Inverse Kinematics
         Robot programming using Kuka Robot Language (KRL)


No designated text book, but class notes and handouts will be provided.


1.     R.N. Jazar, Theory of applied robotics: kinematics, dynamics, and control, Springer Science & Business Media, 2010.
2.     M.W. Spong, and M. Vidyasagar, Robot dynamics and control, John Wiley & Sons, 2008
3.     B. Siciliano, O. Khatib, Springer Handbook of Robotics,Springer, 2008


1.     Transactions on Robotics and Automation, IEEE
2.     Transactions on Mechatronics, IEEE/ASME
3.     Spectrum, IEEE


Lectures: 30 hours
Laboratory sessions: 45 hours
Self study and assignments: 90 hours


Methods used are lectures, laboratory work and assignments which include presentations and conducting computer simulations.


Mid semester examination (20%), final examination (40%) (both are closed book), laboratory sessions (20%) and assignments (20%).

In the evaluation, an “A” will be awarded if a student demonstrates an excellent level of understanding of the principles and demonstrates excellent capabilities in robotics related applications. “B” will be awarded if a student demonstrates an average level of understanding of the principles and demonstrates average capabilities in robotics related applications. “C” will be given if a student demonstrates below average level of understanding of the principles and demonstrates below average level of capabilities in robotics related applications.