Course10610700160 lectures
Introduction to Application of Artificial Intelligence to Robotics
Prof. Pushparaj Mani Pathak · IIT Roorkee
About
Robots are gradually entering into our daily life. Days are not far when they will be part of our family. A robot is an integration of mechanical, electrical and software components that can be reprogrammed to perform a variety of tasks both with and without human intervention. Robotics is the study of robot’s design, programming and control. Essentially a robot contains manipulators, sensors, control systems, power supplies and software all working together to perform a task. Manipulator provides movement. A robot needs to be able to move around its environment. Whether by joint movement, or rolling on wheels, or walking on legs or propelling by thrusters a robot needs to be able to move. Sensing is required to sense its surroundings and find its own location. Control system provides intelligence. Artificial Intelligence can be provided through object recognition, path planning, interacting robot through NLP, navigating the robot and giving emotion to robot. This course will cater the needs of students who are interested in studying the complete kinematics, dynamics, control and implementing AI to the robots and then will like to make one. With this foundation course students will be ready to study advance robotics courses and AI controlled robots.
Intended audience: Mechanical/Electrical/Electronics/Computer Science under graduate and post graduate students
Lectures
Week 1
- Introduction to Robotics
- Introduction to Artificial Intelligence
- Mathematical Background
- Python as Modelling and Simulation Tool
- Introduction to Robot Operating System (ROS)
Week 2
- Spatial Representation
- Robot Kinematics
- Robot Inverse Kinematics
- Jacobian and Singularity
- Static Forces in Manipulator
Week 3
- Acceleration of a Rigid body
- Newton-Euler's Equation
- Newton-Euler's Equation: Examples
- Lagrangian Formulation of Manipulator Dynamics
- Manipulator Dynamic Equations in State Space Form
Week 4
- Joint-Space Schemes
- Cartesian Space Schemes
- Manipulator-Mechanism Deisgn-I
- Manipulator-Mechanism Deisgn-II
- Manipulator Design Example
Week 5
- Linear Control of Manipulators
- Modeling and Control of a Single Joint
- Nonlinear and Time-Varying Systems
- The Control Problem for Manipulators
- Cartesian-Based Control Systems
Week 6
- Fundamentals of Machine Learning-I
- Fundamentals of Machine Learning-II
- Libraries and tools for ML implementation in Python
- Introduction to Decision Trees and Random Forests
- Reinforcement Learning
Week 7
- Introduction to Computer Vision
- Overview of Image Processing
- Image Feature Extraction
- Fundamentals of Neural Networks
- Object Detection Using YOLOv8
Week 8
- Path planning, Graph Searches, and the A* Algorithm
- Obstacle Avoidance
- Introduction to Deep Reinforcement Learning
- GPS Path Finding
- Fuzzy Logic Control
Week 9
- Basics of Genetic Algorithms
- Genetic Algorithms for Path Planning of Robots
- Implementing Neural Networks for Navigation
- Understanding the Perception and Localization
- Mapping, SLAM and Floor Finder Technique
Week 10
- Natural Language Processing
- Transformers
- AI based Speech Recognition
- Conversational AI & Human-Robot Interaction
- Developing An Emotion Engine For Robots
Week 11
- Introducing the Hardware for the Robot
- Sensors used in Robots
- Electrical Actuators
- Machine Learning for Robot Arms
- Implementation of AI to Robot Dynamics
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Roorkee
- Department
- Computer Science and Engineering
- Teacher
- Prof. Pushparaj Mani Pathak
- Level
- Undergraduate
- Language
- English
- Subjects
- Computer science, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed