Comprehensive Course Structure
The B.Tech Robotics program is structured over 8 semesters, combining foundational sciences, core engineering principles, specialized robotics courses, and practical applications. The curriculum balances theoretical understanding with hands-on experience to ensure students are well-prepared for careers in robotics and automation.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MTH101 | Calculus and Analytical Geometry | 4-0-0-4 | - |
| 1 | PHY101 | Physics for Engineering | 3-0-0-3 | - |
| 1 | CSE101 | Introduction to Programming | 2-0-2-4 | - |
| 1 | ME101 | Engineering Drawing and Graphics | 2-0-2-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | ENG101 | English Communication Skills | 2-0-0-2 | - |
| 1 | ME102 | Engineering Mechanics | 3-0-0-3 | MTH101, PHY101 |
| 1 | CSE102 | Data Structures and Algorithms | 3-0-2-5 | CSE101 |
| 2 | MTH201 | Linear Algebra and Differential Equations | 4-0-0-4 | MTH101 |
| 2 | PHY201 | Electromagnetic Fields and Waves | 3-0-0-3 | PHY101 |
| 2 | CSE201 | Object-Oriented Programming with C++ | 2-0-2-4 | CSE101 |
| 2 | ME201 | Mechanics of Materials | 3-0-0-3 | ME102 |
| 2 | EE201 | Basic Electrical Engineering | 3-0-0-3 | - |
| 2 | CHM201 | Organic Chemistry | 3-0-0-3 | CHM101 |
| 3 | MTH301 | Probability and Statistics | 3-0-0-3 | MTH201 |
| 3 | ME301 | Thermodynamics | 3-0-0-3 | ME102 |
| 3 | CSE301 | Database Management Systems | 3-0-2-5 | CSE102 |
| 3 | EE301 | Electronics Circuits | 3-0-2-5 | EE201 |
| 3 | ME302 | Mechanics of Machines | 3-0-0-3 | ME201 |
| 3 | CHM301 | Inorganic Chemistry | 3-0-0-3 | CHM201 |
| 4 | MTH401 | Numerical Methods | 3-0-0-3 | MTH301 |
| 4 | ME401 | Manufacturing Processes | 3-0-0-3 | ME302 |
| 4 | CSE401 | Computer Architecture | 3-0-2-5 | CSE201 |
| 4 | EE401 | Control Systems | 3-0-0-3 | EE301 |
| 4 | ME402 | Robotics Fundamentals | 3-0-2-5 | ME302, EE301 |
| 4 | CHM401 | Physical Chemistry | 3-0-0-3 | CHM301 |
| 5 | CSE501 | Artificial Intelligence | 3-0-2-5 | CSE401, MTH401 |
| 5 | ME501 | Advanced Dynamics and Control | 3-0-0-3 | ME402 |
| 5 | EE501 | Signal Processing | 3-0-2-5 | EE401 |
| 5 | ME502 | Sensors and Actuators | 3-0-2-5 | ME402, EE301 |
| 5 | CSE502 | Machine Learning | 3-0-2-5 | CSE501 |
| 6 | ME601 | Autonomous Navigation | 3-0-2-5 | ME501, ME502 |
| 6 | CSE601 | Computer Vision | 3-0-2-5 | CSE501, CSE502 |
| 6 | EE601 | Embedded Systems | 3-0-2-5 | EE401, CSE401 |
| 6 | ME602 | Human-Robot Interaction | 3-0-2-5 | ME501, ME502 |
| 7 | ME701 | Robotic Manipulation | 3-0-2-5 | ME601, ME602 |
| 7 | CSE701 | Reinforcement Learning | 3-0-2-5 | CSE502 |
| 7 | EE701 | Robotics Hardware Design | 3-0-2-5 | EE601, ME602 |
| 8 | ME801 | Capstone Project | 4-0-0-4 | All previous courses |
| 8 | CSE801 | Research Methodology | 2-0-0-2 | CSE701 |
| 8 | EE801 | Advanced Control Theory | 3-0-0-3 | EE601, ME501 |
Advanced Departmental Elective Courses
These advanced courses provide students with specialized knowledge in various aspects of robotics and automation. Each course is designed to deepen understanding and practical skills relevant to the field.
Artificial Intelligence for Robotics
This course explores how AI techniques can be applied to robotics, covering topics such as machine learning algorithms, neural networks, deep learning architectures, and their integration with robotic systems. Students learn to implement intelligent behaviors in robots using AI tools and frameworks.
Computer Vision in Robotics
Students study image processing techniques, feature detection, object recognition, and scene understanding methods used in robotics applications. The course includes practical implementation of computer vision algorithms for robot perception and navigation.
Reinforcement Learning for Autonomous Robots
This advanced elective focuses on teaching robots to learn optimal policies through interaction with their environment. Students explore Q-learning, policy gradients, and actor-critic methods in the context of robotic control tasks.
Human-Robot Interaction Design
The course covers principles of designing interactive robots that can communicate effectively with humans. Topics include gesture recognition, natural language processing, emotional intelligence, and user-centered design for human-robot interfaces.
Mobile Robotics and Navigation
This course introduces students to the design and implementation of mobile robots, including kinematics, localization, mapping, and path planning algorithms. Students work with platforms like TurtleBot and ROS to build autonomous navigation systems.
Robotic Manipulation and Control
Focused on robotic arms and grippers, this course covers inverse kinematics, trajectory planning, force control, and manipulation strategies. Practical sessions involve building and controlling manipulator robots using real hardware.
Cognitive Robotics
This advanced topic explores how robots can simulate human-like cognitive functions such as memory, reasoning, decision-making, and problem-solving. Students examine both theoretical models and practical implementations of cognitive robotics systems.
Industrial Automation with Robotics
The course bridges the gap between traditional automation and modern robotics in industrial settings. It covers PLC programming, SCADA systems, collaborative robots (cobots), and integration of robotics into smart manufacturing processes.
Embedded Systems for Robotics
Students learn to design and develop embedded software for robotic platforms. Topics include microcontroller architectures, real-time operating systems, sensor interfacing, communication protocols, and power management in robotics applications.
Advanced Control Theory for Robotics
This course delves into modern control strategies such as adaptive control, robust control, and optimal control for complex robotic systems. Students gain proficiency in designing controllers that ensure stability and performance under varying conditions.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means of enhancing practical understanding and fostering innovation among students. The curriculum integrates mini-projects from the second year onwards, culminating in a capstone project in the final year.
Mini-projects are assigned during each semester and typically span 6-8 weeks. These projects focus on applying theoretical concepts to real-world problems, encouraging students to collaborate with peers and seek guidance from faculty mentors. Each project is evaluated based on innovation, technical execution, documentation, and presentation skills.
The final-year thesis or capstone project requires students to propose a significant research problem or application in robotics. Students work under the supervision of faculty members, often collaborating with industry partners or research institutions. The project involves extensive literature review, experimentation, prototyping, testing, and documentation.
Project selection is done through a structured process involving student preferences, availability of faculty mentors, and alignment with departmental research interests. Students are encouraged to explore emerging trends in robotics and propose innovative solutions that address real-world challenges.