Comprehensive Course Structure
The Robotics program at LAKSHMI NARAIN COLLEGE OF TECHNOLOGY AND SCIENCE RIT spans eight semesters, offering a comprehensive and structured curriculum designed to build strong foundational knowledge followed by specialized expertise. The following table provides an overview of all courses offered across the program, including core subjects, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | MA101 | Mathematics I | 3-1-0-4 | - |
| I | PH101 | Physics for Engineers | 3-1-0-4 | - |
| I | CE101 | Introduction to Computer Engineering | 2-0-2-3 | - |
| I | CS101 | Programming Fundamentals in C/C++ | 2-0-2-3 | - |
| I | EE101 | Electrical Circuits and Networks | 3-1-0-4 | - |
| I | ME101 | Engineering Mechanics | 3-1-0-4 | - |
| I | PH102 | Practical Physics Lab | 0-0-3-1 | - |
| I | CS102 | Computer Programming Lab | 0-0-3-1 | - |
| II | MA102 | Mathematics II | 3-1-0-4 | MA101 |
| II | PH103 | Electromagnetic Fields and Waves | 3-1-0-4 | PH101 |
| II | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| II | EE201 | Digital Electronics | 3-1-0-4 | EE101 |
| II | ME201 | Mechanics of Materials | 3-1-0-4 | ME101 |
| II | CE201 | Introduction to Robotics | 2-0-2-3 | CS101, EE101 |
| II | PH104 | Electronics Lab | 0-0-3-1 | - |
| III | MA201 | Mathematics III | 3-1-0-4 | MA102 |
| III | CS301 | Object-Oriented Programming in C++ | 3-1-0-4 | CS201 |
| III | EE301 | Control Systems | 3-1-0-4 | EE201, MA102 |
| III | ME301 | Mechanics of Machines | 3-1-0-4 | ME201 |
| III | CS302 | Database Management Systems | 3-1-0-4 | CS201 |
| III | EE302 | Signals and Systems | 3-1-0-4 | MA102, EE201 |
| III | CS303 | Computer Architecture | 3-1-0-4 | EE201 |
| III | ME302 | Thermodynamics | 3-1-0-4 | ME201 |
| IV | MA202 | Mathematics IV | 3-1-0-4 | MA201 |
| IV | CS401 | Operating Systems | 3-1-0-4 | CS301 |
| IV | EE401 | Electrical Machines | 3-1-0-4 | EE201 |
| IV | ME401 | Mechatronics | 3-1-0-4 | ME301 |
| IV | CS402 | Artificial Intelligence | 3-1-0-4 | CS301, MA201 |
| IV | EE402 | Microcontroller and Embedded Systems | 3-1-0-4 | EE301, CS301 |
| IV | CS403 | Computer Vision | 3-1-0-4 | CS301, MA201 |
| V | CS501 | Robot Kinematics and Dynamics | 3-1-0-4 | ME401, CS401 |
| V | EE501 | Robotics Control Systems | 3-1-0-4 | EE301, CS401 |
| V | CS502 | Machine Learning for Robotics | 3-1-0-4 | CS401, MA202 |
| V | ME501 | Advanced Mechanics of Materials | 3-1-0-4 | ME302 |
| V | EE502 | Sensors and Actuators | 3-1-0-4 | EE302, EE401 |
| V | CS503 | Human-Robot Interaction | 3-1-0-4 | CS401 |
| VI | CS601 | Advanced Control Systems | 3-1-0-4 | EE501, CS502 |
| VI | EE601 | Industrial Robotics | 3-1-0-4 | EE501, EE402 |
| VI | CS602 | Reinforcement Learning | 3-1-0-4 | CS502, MA202 |
| VI | ME601 | Robotics Applications in Healthcare | 3-1-0-4 | ME501 |
| VI | EE602 | Mobile Robotics | 3-1-0-4 | EE501 |
| VI | CS603 | Computer Vision for Robotics | 3-1-0-4 | CS403, CS502 |
| VII | CS701 | Research Methodology | 2-0-2-3 | - |
| VII | EE701 | Robotics Capstone Project | 2-0-4-4 | CS502, EE501 |
| VIII | CS801 | Internship in Robotics | 0-0-6-3 | - |
Detailed Course Descriptions
The department offers a range of advanced departmental electives designed to deepen students' understanding of specialized areas within robotics. These courses are taught by faculty members who are experts in their respective fields and have extensive industry experience.
Robot Kinematics and Dynamics
This course delves into the mathematical models used to describe the motion of robotic systems. Students learn about kinematic chains, forward and inverse kinematics, Jacobian matrices, and dynamic modeling techniques. The course emphasizes practical applications through laboratory exercises and simulations using industry-standard software tools.
Robotics Control Systems
This advanced course focuses on designing and implementing control algorithms for robotic systems. Topics include feedback control, PID controllers, state-space models, robust control, and adaptive control strategies. Students gain hands-on experience with real-time control systems and simulation platforms.
Machine Learning for Robotics
This course introduces students to machine learning techniques specifically tailored for robotics applications. It covers supervised and unsupervised learning, neural networks, deep learning architectures, reinforcement learning, and their integration into robotic decision-making processes.
Human-Robot Interaction
Designed to explore the psychological and social aspects of human-robot interaction, this course examines how robots can be designed to communicate effectively with humans. It covers topics such as emotional intelligence in robots, gesture recognition, voice interaction, and user experience design principles.
Advanced Control Systems
This elective builds upon foundational control theory by introducing advanced concepts such as optimal control, nonlinear control, and model predictive control. Students learn to apply these techniques to complex robotic systems and develop controllers for specific applications.
Industrial Robotics
Focused on automation in manufacturing environments, this course covers the design, programming, and integration of industrial robots. It includes hands-on training with leading manufacturers' platforms such as ABB, Fanuc, and KUKA.
Reinforcement Learning
This course explores how robots can learn optimal behaviors through interaction with their environment. Students study Markov decision processes, Q-learning, policy gradients, and actor-critic methods, applying them to robotic control problems.
Robotics Applications in Healthcare
This course examines the role of robotics in healthcare settings, including surgical robotics, rehabilitation robotics, and assistive technology. It covers regulatory standards, safety protocols, and ethical considerations associated with medical robotics.
Mobile Robotics
Students learn about autonomous navigation, mapping, localization, and path planning for mobile robots. The course includes both theoretical concepts and practical implementation using ROS (Robot Operating System) and simulation tools.
Computer Vision for Robotics
This course focuses on image processing and computer vision techniques used in robotics. Topics include feature detection, object recognition, stereo vision, and 3D reconstruction, all applied to robotic perception systems.
Project-Based Learning Philosophy
The department places great emphasis on project-based learning as a core component of the robotics education experience. Students are encouraged to apply theoretical knowledge through hands-on projects that mirror real-world challenges and applications.
Mini-projects are introduced in the third year, allowing students to work in teams on smaller-scale robotic systems or components. These projects typically last several weeks and require students to integrate concepts from multiple disciplines, such as mechanical design, electronics, programming, and control theory.
The final-year thesis/capstone project represents the culmination of the student's academic journey. Students select a topic that aligns with their interests and career goals, often in collaboration with industry partners or research groups. The project involves extensive research, system design, prototyping, testing, and documentation.
Faculty mentors guide students throughout the process, providing expertise and feedback on technical aspects, methodology, and innovation. Students are evaluated based on their technical competence, creativity, teamwork, presentation skills, and overall contribution to the field of robotics.
The department supports project development through dedicated lab spaces, access to advanced equipment, and funding for prototyping materials. Regular milestone reviews ensure that projects stay on track and meet quality standards.