Comprehensive Course Structure Across 8 Semesters
The Robotics program at Bishamber Sahai Diploma Engineering College is structured into eight semesters, each building upon the previous one to ensure a robust and progressive educational experience. The curriculum includes core engineering subjects, departmental electives, science electives, and laboratory components designed to develop both theoretical knowledge and practical skills in robotics.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | MATH101 | Mathematics I | 3-1-0-4 | None |
| I | PHY101 | Physics for Engineering | 3-1-0-4 | None |
| I | CHM101 | Chemistry for Engineers | 3-1-0-4 | None |
| I | ENG101 | English for Engineers | 3-0-0-3 | None |
| I | CSE101 | Introduction to Programming | 3-0-2-4 | None |
| I | ECE101 | Basic Electrical Engineering | 3-1-0-4 | None |
| I | LAB101 | Programming Lab | 0-0-2-2 | CSE101 |
| I | LAB102 | Basic Electrical Lab | 0-0-2-2 | ECE101 |
| II | MATH201 | Mathematics II | 3-1-0-4 | MATH101 |
| II | ECE201 | Electronics for Robotics | 3-1-0-4 | ECE101 |
| II | CSE201 | Data Structures & Algorithms | 3-1-0-4 | CSE101 |
| II | MECH201 | Mechanics of Materials | 3-1-0-4 | PHY101 |
| II | LAB201 | Electronics Lab | 0-0-2-2 | ECE201 |
| II | LAB202 | Data Structures Lab | 0-0-2-2 | CSE201 |
| III | MATH301 | Mathematics III | 3-1-0-4 | MATH201 |
| III | CSE301 | Control Systems | 3-1-0-4 | CSE201 |
| III | ECE301 | Sensors and Actuators | 3-1-0-4 | ECE201 |
| III | MECH301 | Robotics Kinematics | 3-1-0-4 | MECH201 |
| III | LAB301 | Control Systems Lab | 0-0-2-2 | CSE301 |
| III | LAB302 | Sensors Lab | 0-0-2-2 | ECE301 |
| IV | CSE401 | Artificial Intelligence | 3-1-0-4 | CSE201 |
| IV | ECE401 | Digital Signal Processing | 3-1-0-4 | ECE201 |
| IV | MECH401 | Robot Dynamics and Control | 3-1-0-4 | MECH301 |
| IV | LAB401 | AI Lab | 0-0-2-2 | CSE401 |
| IV | LAB402 | Signal Processing Lab | 0-0-2-2 | ECE401 |
| V | CSE501 | Machine Learning | 3-1-0-4 | CSE401 |
| V | ECE501 | Embedded Systems | 3-1-0-4 | ECE201 |
| V | MECH501 | Human-Robot Interaction | 3-1-0-4 | MECH301 |
| V | LAB501 | Machine Learning Lab | 0-0-2-2 | CSE501 |
| V | LAB502 | Embedded Systems Lab | 0-0-2-2 | ECE501 |
| VI | CSE601 | Computer Vision | 3-1-0-4 | CSE501 |
| VI | ECE601 | Robot Simulation and Modeling | 3-1-0-4 | ECE501 |
| VI | MECH601 | Advanced Robotics Applications | 3-1-0-4 | MECH501 |
| VI | LAB601 | Computer Vision Lab | 0-0-2-2 | CSE601 |
| VI | LAB602 | Simulation Lab | 0-0-2-2 | ECE601 |
| VII | CSE701 | Robotics Capstone Project I | 3-0-4-6 | None |
| VII | ECE701 | Advanced Control Systems | 3-1-0-4 | CSE301 |
| VII | MECH701 | Industrial Robotics | 3-1-0-4 | MECH501 |
| VII | LAB701 | Capstone Project Lab I | 0-0-4-4 | CSE701 |
| VIII | CSE801 | Robotics Capstone Project II | 3-0-4-6 | CSE701 |
| VIII | ECE801 | Research Methods in Robotics | 3-1-0-4 | ECE701 |
| VIII | MECH801 | Special Topics in Robotics | 3-1-0-4 | MECH701 |
| VIII | LAB801 | Capstone Project Lab II | 0-0-4-4 | CSE801 |
Detailed Overview of Departmental Elective Courses
The department offers several advanced elective courses that allow students to explore specialized areas within robotics. These courses are designed to provide in-depth knowledge and practical experience relevant to current industry trends.
1. Machine Learning for Robotics (CSE501)
This course focuses on applying machine learning techniques to robotics applications. Students will learn about neural networks, deep learning architectures, reinforcement learning, and computer vision for robotic systems. The course includes hands-on projects using TensorFlow and PyTorch.
2. Embedded Systems in Robotics (ECE501)
This elective explores the design and implementation of embedded systems for robotic applications. Topics include microcontroller programming, real-time operating systems, communication protocols, and hardware-software integration. Students will build and test embedded systems using ARM processors.
3. Human-Robot Interaction (MECH501)
This course examines the design and evaluation of human-robot interaction systems. It covers user experience design, social robotics, ethical considerations in robotics, and assistive technologies. Students will engage in interdisciplinary projects involving psychology, design, and engineering.
4. Computer Vision for Robotics (CSE601)
This course provides an introduction to computer vision techniques used in robotics. Topics include image processing, feature detection, object recognition, and 3D reconstruction. Students will implement computer vision algorithms using OpenCV and learn how to integrate them into robotic systems.
5. Robot Simulation and Modeling (ECE601)
This course teaches students how to model and simulate robotic systems using simulation software like ROS, Gazebo, and MATLAB/Simulink. It covers kinematic and dynamic modeling, sensor simulation, and control system design in virtual environments.
6. Advanced Control Systems (ECE701)
This course builds upon foundational control theory by exploring advanced topics such as nonlinear control, adaptive control, robust control, and optimal control. Students will apply these concepts to robotic systems and learn how to tune controllers for real-world applications.
7. Industrial Robotics (MECH701)
This elective focuses on industrial automation and robotics in manufacturing environments. Topics include programmable logic controllers, SCADA systems, industrial communication protocols, and process automation. Students will work with actual industrial robots and learn how to integrate them into production lines.
8. Special Topics in Robotics (MECH801)
This course covers emerging trends in robotics such as swarm robotics, soft robotics, bio-inspired robotics, and quantum robotics. Students will read recent research papers and conduct independent projects on cutting-edge topics.
9. Robot Kinematics and Dynamics (MECH301)
This foundational course covers the mathematical modeling of robotic systems. It introduces concepts such as forward and inverse kinematics, Jacobians, workspace analysis, and dynamic modeling using Lagrangian mechanics. Students will solve complex kinematic problems and design robotic mechanisms.
10. Sensor Technology for Robotics (ECE301)
This course explores the types and applications of sensors used in robotics. It covers optical sensors, inertial measurement units, ultrasonic sensors, proximity sensors, and tactile sensors. Students will learn how to integrate these sensors into robotic systems for navigation and manipulation.
Project-Based Learning Approach
The department strongly emphasizes project-based learning as a core component of the curriculum. This approach ensures that students gain hands-on experience while developing critical thinking and problem-solving skills.
Mini-Projects (Semesters 3-6)
Throughout semesters 3 to 6, students work on mini-projects that focus on specific aspects of robotics. These projects are typically completed in teams and involve designing, building, testing, and presenting a solution to a real-world problem.
Final-Year Thesis/Capstone Project
The capstone project is the culminating experience for students in their final year. It requires them to integrate knowledge from all previous semesters and apply it to a significant research or development challenge. Students select their projects in consultation with faculty advisors, ensuring alignment with their interests and career goals.
Project Selection Process
Students begin selecting their capstone projects during the seventh semester. They are encouraged to work with industry partners, research labs, or faculty members on innovative projects that address real-world challenges. The selection process involves submitting project proposals, which are reviewed by a committee of faculty advisors.
Evaluation Criteria
Projects are evaluated based on multiple criteria including technical feasibility, innovation, documentation quality, presentation skills, and overall impact. Students must demonstrate both depth of knowledge and ability to apply it in practical contexts.