Introduction​​
The Artificial Intelligence and robotics are the emerging fields of technology
and the blend of these two technologies is the future of the current generation. It
seems to be the most dominant blend in the history of industrial innovations.
The AI and Robotics Development Centre (Centre of Excellence) at Tecnia
Institute of Advanced Studies is an initiative taken with the intent of learning
about the latest technologies of the field like Deep Learning, Machine Learning,
and Robotics. The Centre of Excellence has given students ample opportunities
to not only learn about them but also work with them to develop innovative
things.
An absolute new age of robotization is ready to transform every industrial
process of Industry 4.0. AI-driven robots like Drones and AI driven medical
instruments are considered more efficient than the old robots that used to work
without AI technology. The AI and Robotic development centre will have:
- Establishment
- Process set up
- Training
- Practical knowledge
- Practical demonstration
- Module development
- Creation of Prototype
- Executable Model
- Patent
- Commercial Diffusion
Machine learning conditions the robots in such a way that with timely
evolution, they learn from their own mistakes, thus preventing constant human
intervention and parallel effort. This ensures adaptability in robotics. Along
with these implications, AI and ML powered software products can analyse data
from hundreds of sources and can give future predictions about the growth of
Industry. AI can analyse consumer data and predict about consumer
preferences, product development, and marketing channels.
The students from different departments of Tecnia Institute of Advanced
Studies are trained on technologies like machine learning, deep learning (a
subset of machine learning) and robotics. Scikit-learn provides dozens of built-
in machine learning algorithms and models, called estimators. Each estimator
can be fitted to some data using its fit method. Scikit-learn is used by the
students to create and evaluate the new model. TensorFlow is a low-level
library that is also used by the students to implement machine learning
techniques and algorithms.
| Code |
ARTIFICIAL INTELLIGENCE (BASICS) |
L |
T |
P |
Credits |
| 2 |
– |
– |
2 |
Pre-Requisites​​
- Basic Programming in Python
- Data Structures
Objectives​​
Artificial Intelligence is a major step forward in how computer system adapts, evolves and learns. It has widespread application in almost every industry and is considered to be a big technological shift, similar in scale to past events such as the industrial revolution, the computer age, and the smart phone revolution.
This course will give an opportunity to gain expertise in one of the most fascinating and fastest growing areas of Computer Science through classroom program that covers fascinating and compelling topics related to human intelligence and its applications in industry, defense, healthcare, agriculture and many other areas. This course will give the students a rigorous, advanced and professional graduate-level foundation in Artificial Intelligence.
Learning Outcomes​​
After undergoing this course, the students will be able to:
- Build intelligent agents for search and games
- Solve AI problems through programming with Python
- Learning optimization and inference algorithms for model learning
- Design and develop programs for an agent to learn and act in a structured environment.
Detail Contents
| Topic |
Description |
Duration |
| Introduction |
Concept of AI, history, current status, scope, agents, environments, Problem Formulations, Review of tree and graph structures, State space representation, Search graph and Search tree. |
(3 Hours) |
| Search Algorithms |
Random search, Search with closed and open list, Depth first and Breadth first search, Heuristic search, Best first search, A* algorithm, Game Search. |
(9 Hours) |
| Probabilistic Reasoning |
Probability, conditional probability, Bayes Rule, Bayesian Networks- representation, construction and inference, temporal model, hidden Markov model. |
(12 Hours) |
List of Suggested Books​​
- Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” , 3rd Edition, Prentice Hall
- Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill
- Trivedi, M.C., “A Classical Approach to Artifical Intelligence”, Khanna Publishing House, Delhi.
- Saroj Kaushik, “Artificial Intelligence”, Cengage Learning India, 2011
- David Poole and Alan Mackworth, “Artificial Intelligence: Foundations for Computational Agents”, Cambridge University Press 2010.
Websites for Reference​​
- https://nptel.ac.in/courses/106105077
- https://nptel.ac.in/courses/106106126
- https://aima.cs.berkeley.edu
| Code |
ARTIFICIAL INTELLIGENCE (BASICS) |
L |
T |
P |
Credits |
| 1 |
– |
2 |
2 |
Pre-Requisites​​
- Basic Programming in Python
- Data Structures
Objectives​​
Artificial Intelligence is a major step forward in how computer system adapts, evolves and learns. It has widespread application in almost every industry and is considered to be a big technological shift, similar in scale to past events such as the industrial revolution, the computer age, and the smart phone revolution.
This course will give an opportunity to gain expertise in one of the most fascinating and fastest growing areas of Computer Science through classroom program that covers fascinating and compelling topics related to human intelligence and its applications in industry, defense, healthcare, agriculture and many other areas. This course will give the students a rigorous, advanced and professional graduate-level foundation in Artificial Intelligence.
Learning Outcomes​​
After undergoing this course, the students will be able to:
- Build intelligent agents for search and games
- Solve AI problems through programming with Python
- Learning optimization and inference algorithms for model learning
- Design and develop programs for an agent to learn and act in a structured environment.
Detail Contents
| Topic |
Description |
Duration |
| Markov Decision process |
MDP formulation, utility theory, utility functions, value iteration, policy iteration and partially observable MDPs. |
(10 Hours) |
| Reinforcement Learning |
Random search, Search with closed and open list, Depth first and Breadth first search, Heuristic search, Best first search, A* algorithm, Game Search. |
(10 Hours) |
List of Practicals​​
- Write a programme to conduct uninformed and informed search.
- Write a programme to conduct game search.
- Write a programme to construct a Bayesian network from given data.
- Write a programme to infer from the Bayesian network.
- Write a programme to run value and policy iteration in a grid world.
- Write a programme to do reinforcement learning in a grid world.
List of Suggested Books​​
- Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” , 3rd Edition, Prentice Hall
- Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill
- Trivedi, M.C., “A Classical Approach to Artifical Intelligence”, Khanna Publishing House, Delhi.
- Saroj Kaushik, “Artificial Intelligence”, Cengage Learning India, 2011
- David Poole and Alan Mackworth, “Artificial Intelligence: Foundations for Computational Agents”, Cambridge University Press 2010.
Websites for Reference​​
- https://nptel.ac.in/courses/106105077
- https://nptel.ac.in/courses/106106126
- https://aima.cs.berkeley.edu
- https://ai.berkeley,edu/project_overview.html(for Practical)
| Code |
Robotics (BASICS) |
L |
T |
P |
Credits |
| 2 |
– |
– |
2 |
Pre-Requisites​​
- Basic Engineering Mathematics
- Automation and Control
Objectives​​
The objective of this course is to impart knowledge about industrial robots for their control and design.
Learning Outcomes​​
After the completion of this course, the students will be able to:
- Perform kinematic and dynamic analyses with simulation.
- Design control laws for a robot.
- Integrate mechanical and electrical hardware for a real prototype of robotic device.
- Select a robotic system for given application.
Detail Contents
| Topic |
Description |
Duration |
| Introduction to Robotics |
1.1 Types and components of a robot, Classification of robots, closed-loop and open- loop control systems.
1.2 Kinematics systems; Definition of mechanisms and manipulators, Social issues and safety. |
(7 Hours) |
| Robot Kinematics and Dynamics |
2.1 Kinematic Modelling: Translation and Rotation Representation, Coordinate transformation, DH parameters, Jacobian, Singularity, and Statics
2.2 Dynamic Modelling: Equations of motion: Euler-Lagrange formulation |
(10 Hours) |
| Sensors and Vision System |
3.1 Sensor: Contact and Proximity, Position, Velocity, Force, Tactile etc.
3.2 Introduction to Cameras, Camera calibration,Geometry of Image formation, Euclidean/Similarity/Affine/Projective transformations
3.3 Vision applications in robotics. |
(13 Hours) |
List of Suggested Books​​
- Saha, S.K., “Introduction to Robotics, 2nd Edition, McGraw-Hill Higher Education, New Delhi, 2014.
- Ghosal, A., “Robotics”, Oxford, New Delhi, 2006.
- Niku Saeed B., “Introduction to Robotics: Analysis, Systems, Applications”, PHI, New Delhi.
- Mittal R.K. and Nagrath I.J., “Robotics and Control”, Tata McGraw Hill.
- Mukherjee S., “Robotics and Automation”, Khanna Publishing House, Delhi.
- Craig, J.J., “Introduction to Robotics: Mechanics and Control”, Pearson, New Delhi, 2009
- Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, “Robot Modelling and Control”, John Wiley and Sons Inc, 2005
- Steve Heath, “Embedded System Design”, 2nd Edition, Newnes, Burlington, 2003
- Merzouki R., Samantaray A.K., Phathak P.M. and Bouamama B. Ould, “Intelligent Mechatronic System: Modeling, Control and Diagnosis”, Springer.
| Code |
Robotics (ADVANCED) |
L |
T |
P |
Credits |
| 2 |
– |
– |
2 |
Pre-Requisites​​
- Basic Engineering Mathematics
- Automation and Control
Objectives​​
The objective of this course is to impart knowledge about industrial robots for their control and design.
Learning Outcomes​​
After the completion of this course, the students will be able to:
- Perform kinematic and dynamic analyses with simulation.
- Design control laws for a robot.
- Integrate mechanical and electrical hardware for a real prototype of robotic device.
- Select a robotic system for given application.
Detail Contents
| Topic |
Description |
Duration |
| Robot Control |
4.1 Basics of control: Transfer functions, Control laws: P, PD, PID
4.2 Non-linear and advanced controls |
(12 Hours) |
| Robot Actuation Systems |
Actuators: Electric, Hydraulic and Pneumatic; Transmission: Gears, Timing Belts and Bearings, Parameters for selection of actuators |
(8 Hours) |
| Control Hardware and Interfacing |
Embedded systems: Architecture and integration with sensors, actuators, components, Programming for Robot Applications |
(10 Hours) |
List of Suggested Books​​
- Saha, S.K., “Introduction to Robotics, 2nd Edition, McGraw-Hill Higher Education, New Delhi, 2014.
- Ghosal, A., “Robotics”, Oxford, New Delhi, 2006.
- Niku Saeed B., “Introduction to Robotics: Analysis, Systems, Applications”, PHI, New Delhi.
- Mittal R.K. and Nagrath I.J., “Robotics and Control”, Tata McGraw Hill.
- Mukherjee S., “Robotics and Automation”, Khanna Publishing House, Delhi.
- Craig, J.J., “Introduction to Robotics: Mechanics and Control”, Pearson, New Delhi, 2009
- Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, “Robot Modelling and Control”, John Wiley and Sons Inc, 2005
- Steve Heath, “Embedded System Design”, 2nd Edition, Newnes, Burlington, 2003
- Merzouki R., Samantaray A.K., Phathak P.M. and Bouamama B. Ould, “Intelligent Mechatronic System: Modeling, Control and Diagnosis”, Springer.
