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.