Comprehensive Course Listing Across All Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | PHYS101 | Engineering Physics | 3-1-0-4 | None |
| 1 | MATH101 | Calculus and Linear Algebra | 4-0-0-4 | None |
| 1 | CSE101 | Introduction to Programming | 2-0-2-3 | None |
| 1 | MECH101 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | ENGG101 | Introduction to Engineering | 2-0-0-2 | None |
| 2 | MATH201 | Differential Equations | 3-0-0-3 | MATH101 |
| 2 | PHYS201 | Modern Physics | 3-1-0-4 | PHYS101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | MECH201 | Mechanics of Materials | 3-1-0-4 | MECH101 |
| 2 | ENGG201 | Engineering Drawing and CAD | 2-0-2-3 | ENGG101 |
| 3 | MATH301 | Probability and Statistics | 3-0-0-3 | MATH201 |
| 3 | ELEC301 | Electrical Circuits and Electronics | 3-1-0-4 | PHYS201 |
| 3 | CSE301 | Object-Oriented Programming | 2-0-2-3 | CSE201 |
| 3 | MECH301 | Thermodynamics | 3-1-0-4 | MECH201 |
| 3 | ENGG301 | Engineering Materials | 3-1-0-4 | ENGG201 |
| 4 | MATH401 | Numerical Methods | 3-0-0-3 | MATH301 |
| 4 | ELEC401 | Control Systems | 3-1-0-4 | ELEC301 |
| 4 | CSE401 | Database Management Systems | 2-0-2-3 | CSE301 |
| 4 | MECH401 | Mechanics of Machines | 3-1-0-4 | MECH301 |
| 4 | ENGG401 | Industrial Management | 2-0-0-2 | ENGG301 |
| 5 | CSE501 | Computer Vision | 3-1-0-4 | CSE401 |
| 5 | ELEC501 | Signal Processing | 3-1-0-4 | ELEC401 |
| 5 | MECH501 | Robot Kinematics and Dynamics | 3-1-0-4 | MECH401 |
| 5 | ENGG501 | Project Management | 2-0-0-2 | ENGG401 |
| 6 | CSE601 | Machine Learning | 3-1-0-4 | CSE501 |
| 6 | ELEC601 | Embedded Systems | 3-1-0-4 | ELEC501 |
| 6 | MECH601 | Advanced Control Theory | 3-1-0-4 | MECH501 |
| 6 | ENGG601 | Research Methodology | 2-0-0-2 | ENGG501 |
| 7 | CSE701 | Robotics and AI | 3-1-0-4 | CSE601 |
| 7 | ELEC701 | Intelligent Control Systems | 3-1-0-4 | ELEC601 |
| 7 | MECH701 | Robot Design and Fabrication | 3-1-0-4 | MECH601 |
| 7 | ENGG701 | Entrepreneurship in Robotics | 2-0-0-2 | ENGG601 |
| 8 | CSE801 | Capstone Project | 4-0-0-4 | CSE701 |
| 8 | ELEC801 | Advanced Robotics | 3-1-0-4 | ELEC701 |
| 8 | MECH801 | Final Year Thesis | 6-0-0-6 | MECH701 |
| 8 | ENGG801 | Industrial Internship | 2-0-0-2 | ENGG701 |
Advanced Departmental Elective Courses
These advanced courses build upon foundational knowledge and offer specialized skills required in modern robotics engineering:
- Deep Learning for Robots: This course explores neural networks, convolutional and recurrent architectures tailored for robotic applications. Students learn to implement and train models on real-time sensor data.
- Natural Language Processing in Robotics: Focuses on integrating language understanding into robot behavior, enabling robots to comprehend commands and communicate naturally with users.
- Reinforcement Learning Algorithms: Covers Q-learning, policy gradients, actor-critic methods, and their implementation in robotic systems for adaptive control and decision-making.
- Human-Centered Robotics: Examines the design principles of robots that interact effectively with humans, including user interface design, empathy modeling, and social robot behavior.
- Cognitive Modeling in HRI: Investigates how cognitive science concepts apply to human-robot interaction, focusing on perception, memory, and decision-making in collaborative scenarios.
- Path Planning Algorithms: Teaches algorithms for navigation in dynamic environments, including A*, Dijkstra's, RRT (Rapidly-exploring Random Tree), and optimization techniques.
- SLAM Techniques: Introduces simultaneous localization and mapping using sensors like LiDAR, cameras, and IMUs to enable autonomous navigation.
- Mobile Robotics: Covers mobile robot kinematics, dynamics, control, and applications in logistics, surveillance, and search-and-rescue missions.
- Underwater Vehicle Design: Focuses on designing vehicles for oceanic exploration, including hydrodynamics, propulsion systems, and communication technologies.
- Soft Robotics: Explores flexible materials, bio-inspired mechanisms, and applications in rehabilitation, prosthetics, and delicate manipulation tasks.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes experiential education, integrating theoretical concepts with practical implementation. Mini-projects span across semesters, beginning with simple design challenges and progressing to complex multi-disciplinary solutions.
Mini-project structure involves:
- Team formation based on interest alignment
- Quarterly milestone reviews
- Peer evaluation and feedback mechanisms
- Presentation at departmental symposiums
The final-year thesis or capstone project requires students to propose, design, develop, and present a significant contribution to robotics. Projects are selected in collaboration with faculty mentors based on student interests and current research areas.
Evaluation criteria include:
- Technical depth of solution
- Innovation and creativity
- Documentation quality
- Presentation clarity
- Impact on real-world problems