Curriculum Overview
The Robotics program at JAWAHARLAL INSTITUTE OF TECHNOLOGY BORAWAN is designed to provide students with a comprehensive understanding of robotics principles and their practical applications. The curriculum spans four years, with each semester building upon the previous one to ensure a progressive learning experience.
Each year is divided into two semesters, totaling eight semesters over the course of the program. Students are required to complete core courses, departmental electives, science electives, and laboratory components to fulfill graduation requirements.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Engineering Mathematics I | 4-0-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CSE101 | Computer Programming | 2-0-2-4 | - |
| 1 | ME101 | Basic Mechanics | 3-0-0-3 | - |
| 1 | ECE101 | Basic Electronics | 3-0-0-3 | - |
| 1 | HSS101 | English Communication Skills | 2-0-0-2 | - |
| 2 | MATH201 | Engineering Mathematics II | 4-0-0-4 | MATH101 |
| 2 | PHY201 | Modern Physics and Quantum Mechanics | 3-0-0-3 | PHY101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | ME201 | Strength of Materials | 3-0-0-3 | ME101 |
| 2 | ECE201 | Circuit Analysis | 3-0-0-3 | ECE101 |
| 2 | ME202 | Mechanics of Machines | 3-0-0-3 | ME101 |
| 3 | MATH301 | Probability and Statistics | 3-0-0-3 | MATH201 |
| 3 | ME301 | Control Systems | 3-0-0-3 | ME201, ECE201 |
| 3 | ECE301 | Digital Logic Design | 3-0-0-3 | ECE201 |
| 3 | CSE301 | Object-Oriented Programming | 2-0-2-4 | CSE201 |
| 3 | HSS301 | Social Sciences | 2-0-0-2 | - |
| 3 | ME302 | Thermodynamics and Heat Transfer | 3-0-0-3 | ME201 |
| 4 | MATH401 | Advanced Calculus | 4-0-0-4 | MATH301 |
| 4 | ME401 | Robotics and Automation | 3-0-0-3 | ME301, ECE301 |
| 4 | ECE401 | Signals and Systems | 3-0-0-3 | ECE201 |
| 4 | CSE401 | Database Management Systems | 3-0-0-3 | CSE301 |
| 4 | HSS401 | Human Values and Ethics | 2-0-0-2 | - |
| 5 | ME501 | Advanced Control Theory | 3-0-0-3 | ME401 |
| 5 | ECE501 | Microprocessor and Microcontroller Applications | 3-0-2-5 | ECE401, CSE301 |
| 5 | CSE501 | Artificial Intelligence | 3-0-0-3 | CSE401 |
| 5 | ME502 | Sensor and Actuator Technology | 3-0-0-3 | ME302 |
| 5 | CSE502 | Computer Vision | 3-0-0-3 | CSE401, CSE301 |
| 6 | ME601 | Mobile Robot Navigation | 3-0-0-3 | ME501, ME502 |
| 6 | ECE601 | Embedded Systems Design | 3-0-2-5 | ECE501 |
| 6 | CSE601 | Machine Learning | 3-0-0-3 | CSE501 |
| 6 | ME602 | Human-Robot Interaction | 3-0-0-3 | ME401 |
| 7 | ME701 | Advanced Robotics Design | 3-0-0-3 | ME601 |
| 7 | ECE701 | Robotics Software Engineering | 3-0-2-5 | ECE601 |
| 7 | CSE701 | Natural Language Processing | 3-0-0-3 | CSE601 |
| 7 | ME702 | Bio-inspired Robotics | 3-0-0-3 | ME602 |
| 8 | ME801 | Final Year Project (Capstone) | 4-0-0-4 | All previous courses |
| 8 | ECE801 | Robotics Research Methods | 3-0-0-3 | ECE701 |
| 8 | CSE801 | Advanced AI Applications | 3-0-0-3 | CSE701 |
| 8 | ME802 | Robotics Ethics and Policy | 2-0-0-2 | ME702 |
Advanced Departmental Elective Courses
Departmental electives offer students the opportunity to delve deeper into specific areas of robotics, tailored to their interests and career goals. Here are detailed descriptions of several advanced elective courses:
Machine Learning for Robotics (CSE501): This course introduces students to machine learning techniques specifically adapted for robotic applications. Topics include supervised and unsupervised learning algorithms, neural networks, reinforcement learning, and deep learning models applied to robot perception and control. Students will implement these concepts using Python frameworks like TensorFlow and PyTorch.
Computer Vision for Robots (CSE502): Focused on image processing and computer vision techniques used in robotics, this course covers feature extraction, object recognition, stereo vision, and 3D reconstruction. Students will work with datasets from real-world robotic applications and use tools like OpenCV and MATLAB to develop visual perception systems.
Human-Robot Interaction (ME602): This course explores the design of interfaces and behaviors that enable effective communication between humans and robots. It includes topics such as gesture recognition, speech processing, emotional modeling, and user experience design for robotic systems. Practical components involve developing interactive prototypes and conducting user studies.
Mobile Robot Navigation (ME601): Students learn how to program autonomous robots to navigate unknown environments using sensors like LIDAR, cameras, and IMUs. The course covers SLAM algorithms, path planning techniques, localization methods, and simulation environments such as ROS and Gazebo.
Advanced Control Theory (ME501): This advanced course builds upon foundational control systems theory by exploring robust control, optimal control, nonlinear control, and adaptive control strategies. Students will model complex systems and design controllers for robotic applications using MATLAB/Simulink and other simulation tools.
Embedded Systems Design (ECE601): This course focuses on designing embedded systems for robotics applications, covering microcontrollers, real-time operating systems, interrupt handling, memory management, and hardware-software integration. Practical projects involve building robot controllers using ARM Cortex-M processors and Arduino platforms.
Bio-inspired Robotics (ME702): Inspired by nature, this course examines how biological principles can be applied to create innovative robotic designs. Students will study animal locomotion, swarm behavior, sensory systems, and biomimetic structures, applying this knowledge to design robots with enhanced mobility or functionality.
Robotics Software Engineering (ECE701): This course addresses the software engineering challenges in robotics development, including version control, testing methodologies, documentation standards, and project management. Students will work in teams to develop full-stack robotic applications using agile development practices and CI/CD pipelines.
Advanced AI Applications (CSE801): This capstone elective integrates advanced artificial intelligence concepts into robotics projects. Topics include generative adversarial networks (GANs), transformers, reinforcement learning for complex environments, and ethical considerations in AI deployment. Students will propose and execute original research projects related to AI-enhanced robotics.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a cornerstone of the curriculum. This approach ensures that students apply theoretical knowledge to solve real-world problems while developing critical thinking, teamwork, and communication skills.
Students begin their journey with mini-projects in the second year, working in small groups to design and build simple robotic systems. These projects are evaluated based on technical execution, innovation, documentation quality, and presentation effectiveness.
The final-year thesis/capstone project is a significant component of the program. Students select a topic aligned with their interests or industry needs, often collaborating with faculty members or external organizations. The process includes proposal development, literature review, system design, implementation, testing, and final reporting.
Faculty mentors guide students through each phase of the project, ensuring academic rigor and practical relevance. Regular progress reviews and milestone assessments are conducted to maintain quality and timeliness.