Curriculum Overview
The Robotics program at BHABHA ENGINEERING RESEARCH INSTITUTE is meticulously structured over eight semesters, ensuring a comprehensive blend of theoretical knowledge and practical application. The curriculum includes core courses, departmental electives, science electives, and mandatory labs designed to develop both technical and soft skills essential for a successful career in robotics.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 4-0-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CSE101 | Introduction to Programming | 2-0-2-4 | - |
| 1 | MAT101 | Calculus and Differential Equations | 4-0-0-4 | - |
| 1 | ECE101 | Basic Electronics | 3-0-0-3 | - |
| 2 | ENG102 | Engineering Mathematics II | 4-0-0-4 | ENG101, MAT101 |
| 2 | PHY102 | Modern Physics | 3-0-0-3 | PHY101 |
| 2 | CSE102 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | MAT102 | Linear Algebra and Vector Calculus | 4-0-0-4 | MAT101 |
| 2 | ECE102 | Circuit Analysis | 3-0-0-3 | ECE101 |
| 3 | ENG201 | Signals and Systems | 4-0-0-4 | ENG102, MAT102 |
| 3 | MAT201 | Probability and Statistics | 3-0-0-3 | MAT102 |
| 3 | CSE201 | Object-Oriented Programming | 3-0-0-3 | CSE102 |
| 3 | MCH201 | Engineering Mechanics | 4-0-0-4 | - |
| 3 | ECE201 | Control Systems | 3-0-0-3 | ECE102, ENG102 |
| 4 | ENG202 | Electromagnetic Fields | 3-0-0-3 | ENG102, MAT102 |
| 4 | MAT202 | Complex Analysis | 3-0-0-3 | MAT102 |
| 4 | CSE202 | Database Management Systems | 3-0-0-3 | CSE102 |
| 4 | MCH202 | Thermodynamics and Fluid Mechanics | 4-0-0-4 | MCH201 |
| 4 | ECE202 | Digital Electronics | 3-0-0-3 | ECE102 |
| 5 | ENG301 | Robotics Fundamentals | 3-0-0-3 | ENG201, ECE201, CSE201 |
| 5 | MCH301 | Robot Kinematics and Dynamics | 3-0-0-3 | MCH202, ENG201 |
| 5 | CSE301 | Artificial Intelligence | 3-0-0-3 | CSE202 |
| 5 | ECE301 | Sensors and Actuators | 3-0-0-3 | ECE202 |
| 5 | MAT301 | Numerical Methods | 3-0-0-3 | MAT201 |
| 6 | ENG302 | Control Systems in Robotics | 3-0-0-3 | ENG301, ECE301 |
| 6 | MCH302 | Robot Manipulation and Motion Planning | 3-0-0-3 | MCH301 |
| 6 | CSE302 | Machine Learning | 3-0-0-3 | CSE301 |
| 6 | ECE302 | Embedded Systems | 3-0-0-3 | ECE202 |
| 6 | MAT302 | Optimization Techniques | 3-0-0-3 | MAT301 |
| 7 | ENG401 | Advanced Robotics Concepts | 3-0-0-3 | ENG302, CSE302 |
| 7 | MCH401 | Human-Robot Interaction | 3-0-0-3 | MCH302 |
| 7 | CSE401 | Deep Learning for Robotics | 3-0-0-3 | CSE302 |
| 7 | ECE401 | Computer Vision in Robotics | 3-0-0-3 | ECE302 |
| 7 | MAT401 | Advanced Control Theory | 3-0-0-3 | MAT302 |
| 8 | ENG402 | Capstone Project | 6-0-0-6 | ENG401, CSE401 |
| 8 | MCH402 | Robotics Thesis | 6-0-0-6 | MCH401 |
| 8 | CSE402 | Special Topics in Robotics | 3-0-0-3 | CSE401 |
| 8 | ECE402 | Robotics Internship | 6-0-0-6 | ECE401 |
| 8 | MAT402 | Research Methodology | 3-0-0-3 | - |
The department emphasizes project-based learning throughout the program, with students engaging in both mini-projects and a final-year thesis/capstone project. The mini-project phase begins in the third year, where students work on small-scale robotics challenges under faculty supervision.
These projects are designed to integrate knowledge from multiple disciplines, allowing students to apply theoretical concepts to real-world problems. Projects can range from designing simple robotic arms to developing autonomous navigation systems for drones or underwater robots.
The final-year capstone project is a significant milestone that requires students to work in teams on a large-scale robotics initiative. Students are encouraged to collaborate with industry partners, research labs, or faculty-led projects. The project typically spans the entire semester and involves extensive planning, implementation, testing, and documentation.
Faculty mentors play a crucial role in guiding students through these projects. They provide technical support, suggest relevant resources, and ensure that the projects align with academic standards and industry expectations.
Advanced Departmental Elective Courses
Several advanced departmental elective courses are offered to deepen students' expertise in specialized areas of robotics:
- Deep Learning for Robotics: This course explores how neural networks can be applied to robotics, including convolutional and recurrent architectures for perception and control. Students learn to implement algorithms using frameworks like TensorFlow and PyTorch.
- Human-Robot Interaction: Focused on the design and evaluation of interactive systems between humans and robots, this course covers topics such as user experience design, ethical considerations, and social robotics.
- Robotic Perception Systems: This elective delves into sensor fusion techniques, computer vision algorithms, and SLAM (Simultaneous Localization and Mapping) methods used in robotic navigation.
- Autonomous Navigation: Students study path planning algorithms, motion control strategies, and decision-making systems for autonomous robots operating in dynamic environments.
- Industrial Robotics and Automation: This course covers the application of robotics in manufacturing settings, including PLC programming, robot kinematics, and integration with industrial control systems.
- Reinforcement Learning for Robotics: A focus on developing adaptive control policies using reinforcement learning techniques, enabling robots to learn optimal behaviors through interaction with their environment.
- Cognitive Robotics: This course introduces the intersection of cognitive science and robotics, examining how robots can simulate human-like reasoning and learning capabilities.
- Soft Robotics and Bio-Inspired Design: Exploring biomimetic approaches to robot design, this course covers materials science, soft actuation methods, and the development of compliant robotic systems inspired by nature.
- Mobile Robotics: Students learn about mobile platforms, navigation strategies, localization techniques, and communication protocols essential for autonomous mobile robots.
- Robotics Simulation and Modeling: Using tools like ROS (Robot Operating System), students model complex robotic behaviors and simulate environments to test control algorithms before deployment.
Each course is structured around learning objectives that align with industry needs and academic rigor. Assessment methods include written exams, practical assignments, lab reports, presentations, and group projects, ensuring a well-rounded educational experience.