Comprehensive Course Structure for BIMT Robotics Program
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | English for Engineers | 3-0-0-3 | - |
| 1 | MAT101 | Calculus I | 4-0-0-4 | - |
| 1 | MAT102 | Linear Algebra | 3-0-0-3 | - |
| 1 | PHY101 | Physics for Engineers | 4-0-0-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | ECE101 | Basic Electrical Engineering | 4-0-0-4 | - |
| 1 | CS101 | Introduction to Programming | 3-0-2-5 | - |
| 1 | MEC101 | Engineering Graphics & Design | 3-0-2-5 | - |
| 1 | LAB101 | Programming Lab | 0-0-2-2 | - |
| 1 | LAT101 | Basic Electronics Lab | 0-0-2-2 | - |
| 2 | MAT201 | Calculus II | 4-0-0-4 | MAT101 |
| 2 | MAT202 | Differential Equations | 3-0-0-3 | MAT101 |
| 2 | PHY201 | Modern Physics | 3-0-0-3 | PHY101 |
| 2 | ECE201 | Electronics Circuits | 4-0-0-4 | ECE101 |
| 2 | CS201 | Data Structures & Algorithms | 3-0-2-5 | CS101 |
| 2 | MEC201 | Mechanics of Materials | 4-0-0-4 | - |
| 2 | LAB201 | Circuits Lab | 0-0-2-2 | ECE201 |
| 2 | LAT201 | Data Structures Lab | 0-0-2-2 | CS201 |
| 3 | MAT301 | Probability & Statistics | 3-0-0-3 | MAT201 |
| 3 | CS301 | Object-Oriented Programming | 3-0-2-5 | CS201 |
| 3 | ECE301 | Signals & Systems | 4-0-0-4 | ECE201 |
| 3 | MEC301 | Thermodynamics | 4-0-0-4 | - |
| 3 | CS302 | Database Systems | 3-0-2-5 | CS201 |
| 3 | LAB301 | Systems Lab | 0-0-2-2 | ECE301 |
| 3 | LAT301 | Database Lab | 0-0-2-2 | CS302 |
| 4 | MAT401 | Numerical Methods | 3-0-0-3 | MAT201 |
| 4 | CS401 | Operating Systems | 3-0-2-5 | CS301 |
| 4 | ECE401 | Control Systems | 4-0-0-4 | ECE301 |
| 4 | MEC401 | Mechanics of Solids | 4-0-0-4 | MEC201 |
| 4 | CS402 | Computer Networks | 3-0-2-5 | CS301 |
| 4 | LAB401 | Control Systems Lab | 0-0-2-2 | ECE401 |
| 4 | LAT401 | Networks Lab | 0-0-2-2 | CS402 |
| 5 | CS501 | Artificial Intelligence | 3-0-2-5 | CS301 |
| 5 | ECE501 | Digital Signal Processing | 4-0-0-4 | ECE301 |
| 5 | MEC501 | Robotics Kinematics | 4-0-0-4 | MEC401 |
| 5 | CS502 | Machine Learning | 3-0-2-5 | CS501 |
| 5 | LAB501 | AI & ML Lab | 0-0-2-2 | CS502 |
| 5 | LAT501 | Robotics Lab | 0-0-2-2 | MEC501 |
| 6 | CS601 | Embedded Systems | 3-0-2-5 | CS401 |
| 6 | ECE601 | Sensor Technologies | 4-0-0-4 | ECE401 |
| 6 | MEC601 | Industrial Robotics | 4-0-0-4 | MEC501 |
| 6 | CS602 | Computer Vision | 3-0-2-5 | CS501 |
| 6 | LAB601 | Sensors & Control Lab | 0-0-2-2 | ECE601 |
| 6 | LAT601 | Computer Vision Lab | 0-0-2-2 | CS602 |
| 7 | CS701 | Advanced AI Applications | 3-0-2-5 | CS502 |
| 7 | ECE701 | Autonomous Navigation | 4-0-0-4 | ECE601 |
| 7 | MEC701 | Human-Robot Interaction | 4-0-0-4 | MEC601 |
| 7 | CS702 | Reinforcement Learning | 3-0-2-5 | CS701 |
| 7 | LAB701 | Advanced Robotics Lab | 0-0-2-2 | MEC701 |
| 7 | LAT701 | Reinforcement Learning Lab | 0-0-2-2 | CS702 |
| 8 | CS801 | Capstone Project | 3-0-0-6 | - |
| 8 | LAT801 | Final Year Project Lab | 0-0-4-4 | - |
Detailed Course Descriptions
The department's philosophy on project-based learning is centered around experiential education that bridges the gap between theoretical knowledge and practical application. Students engage in hands-on projects from their first year, with increasing complexity and independence as they advance through the program.
Mini-projects are assigned at the end of each semester, providing students with opportunities to apply concepts learned in class. These projects typically span two weeks and involve small teams working on real-world problems under faculty supervision. The evaluation criteria include technical execution, creativity, presentation skills, and teamwork.
The final-year capstone project is a significant undertaking that requires students to integrate knowledge from all disciplines they have studied. Students select their projects in consultation with faculty mentors based on their interests and career aspirations. The project must address a relevant societal challenge or industry need and result in a working prototype or solution.
Project selection process involves a proposal submission phase where students present their ideas, followed by mentor allocation based on faculty expertise and student preferences. The final-year thesis is evaluated by a committee of faculty members, including external experts from industry or academia.
Advanced Departmental Electives
1. Artificial Intelligence in Robotics: This course introduces students to AI concepts applied in robotics, focusing on machine learning algorithms, neural networks, and deep learning for robotic systems. Students will explore real-world applications such as autonomous navigation, object recognition, and decision-making.
2. Reinforcement Learning for Robotics: Designed for advanced students, this course explores reinforcement learning techniques in robotics, including Q-learning, policy gradients, and actor-critic methods. Students will implement algorithms using simulation environments and test them on physical robots.
3. Computer Vision for Robotics: This elective focuses on image processing and computer vision techniques used in robotics applications. Topics include feature extraction, object detection, stereo vision, and camera calibration, with hands-on labs using OpenCV and ROS.
4. Sensor Fusion and Navigation: Students learn how to integrate data from multiple sensors to create accurate navigation systems for robots. The course covers GPS, IMU, LIDAR, and camera-based systems, with emphasis on sensor fusion algorithms and SLAM techniques.
5. Human-Robot Interaction Design: This course explores the design principles behind effective human-robot interfaces. Students will study user experience (UX) design for robots, affective computing, gesture recognition, and ethical considerations in robot deployment.
6. Industrial Automation and Control Systems: Focused on automation in manufacturing environments, this course covers PLC programming, SCADA systems, robotics integration, and process control techniques used in modern factories.
7. Autonomous Vehicle Technologies: Students study the technologies behind self-driving cars, including perception systems, path planning, control algorithms, and safety protocols. The course includes both theoretical concepts and practical implementation using simulation tools.
8. Medical Robotics: This elective focuses on robotics applications in healthcare, including surgical robots, prosthetics, rehabilitation devices, and assistive technologies. Students will explore the intersection of engineering and medicine through case studies and hands-on projects.
9. Maritime Robotics: Designed for students interested in oceanic applications, this course covers underwater robotics, autonomous surface vehicles, sonar systems, and marine sensor technologies. Practical components include simulation exercises and lab experiments.
10. Energy and Environmental Robotics: This course explores how robotics can be used to monitor environmental conditions, manage energy resources, and restore ecosystems. Students will work on projects involving renewable energy systems, pollution monitoring, and sustainable agriculture solutions.
11. Robotic Manipulation and Control: Focused on the mechanics and control of robotic arms and manipulators, this course covers kinematics, dynamics, trajectory planning, and force control techniques. Students will design and implement control systems for robotic manipulators.
12. Mobile Robotics: This elective introduces students to autonomous mobile robots, covering topics such as localization, mapping, path planning, and swarm robotics. Students will build and program mobile robots using ROS and other open-source platforms.
13. Robotics Ethics and Governance: As robotics becomes increasingly integrated into society, ethical considerations become critical. This course examines the moral implications of robotic technologies, including privacy, safety, job displacement, and governance frameworks for autonomous systems.
14. Embedded Systems in Robotics: This course focuses on designing embedded software and hardware for robotic applications. Students will learn microcontroller programming, real-time operating systems, and integration of sensors and actuators in robotics platforms.
15. Advanced Simulation and Modeling: This elective provides students with advanced tools and techniques for simulating robotic systems using MATLAB/Simulink, Gazebo, and other simulation environments. The course emphasizes modeling complex robotic behaviors and validating them before physical implementation.