Curriculum
The Bachelor of Robotics program at Mittal Institute of Technology is structured to provide a comprehensive and progressive academic journey. The curriculum is divided into 8 semesters, with each semester building upon the previous one. This section outlines all core courses, departmental electives, science electives, and laboratory subjects across these semesters.
Semester-wise Course Structure
| SEMESTER | COURSE CODE | COURSE TITLE | CREDIT STRUCTURE (L-T-P-C) | PREREQUISITES |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry for Engineering | 3-1-0-4 | - |
| 1 | CP101 | Computer Programming | 2-0-2-4 | - |
| 1 | MECH101 | Introduction to Mechanics | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Drawing & Graphics | 1-0-2-3 | - |
| 1 | LAB101 | Basic Engineering Lab | 0-0-3-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | PHY201 | Electromagnetism & Optics | 3-1-0-4 | PHY101 |
| 2 | ELEC201 | Basic Electrical Engineering | 3-1-0-4 | - |
| 2 | MATH201 | Probability & Statistics | 3-1-0-4 | ENG101 |
| 2 | CP201 | Data Structures & Algorithms | 2-0-2-4 | CP101 |
| 2 | MECH201 | Strength of Materials | 3-1-0-4 | MECH101 |
| 2 | LAB201 | Electrical Engineering Lab | 0-0-3-2 | - |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ELEC301 | Electronics Devices & Circuits | 3-1-0-4 | ELEC201 |
| 3 | MATH301 | Numerical Methods | 3-1-0-4 | MATH201 |
| 3 | CP301 | Object-Oriented Programming | 2-0-2-4 | CP201 |
| 3 | MECH301 | Thermodynamics | 3-1-0-4 | MECH201 |
| 3 | LAB301 | Electronics Lab | 0-0-3-2 | - |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ELEC401 | Signals & Systems | 3-1-0-4 | ELEC301 |
| 4 | MATH401 | Differential Equations | 3-1-0-4 | MATH301 |
| 4 | CP401 | Database Management Systems | 2-0-2-4 | CP301 |
| 4 | MECH401 | Mechanics of Machines | 3-1-0-4 | MECH301 |
| 4 | LAB401 | Control Systems Lab | 0-0-3-2 | - |
| 5 | ENG501 | Robotics Fundamentals | 3-1-0-4 | ENG401, ELEC401 |
| 5 | ELEC501 | Microprocessors & Microcontrollers | 3-1-0-4 | ELEC401 |
| 5 | CP501 | Artificial Intelligence & Machine Learning | 2-0-2-4 | CP401 |
| 5 | MECH501 | Design of Mechanical Systems | 3-1-0-4 | MECH401 |
| 5 | LAB501 | Robotics Lab I | 0-0-3-2 | - |
| 6 | ENG601 | Sensors & Actuators | 3-1-0-4 | ENG501 |
| 6 | ELEC601 | Control Systems | 3-1-0-4 | ELEC501 |
| 6 | CP601 | Computer Vision | 2-0-2-4 | CP501 |
| 6 | MECH601 | Manufacturing Processes | 3-1-0-4 | MECH501 |
| 6 | LAB601 | Robotics Lab II | 0-0-3-2 | - |
| 7 | ENG701 | Advanced Robotics | 3-1-0-4 | ENG601 |
| 7 | ELEC701 | Embedded Systems | 3-1-0-4 | ELEC601 |
| 7 | CP701 | Reinforcement Learning | 2-0-2-4 | CP601 |
| 7 | MECH701 | Mechatronics Systems | 3-1-0-4 | MECH601 |
| 7 | LAB701 | Robotics Lab III | 0-0-3-2 | - |
| 8 | ENG801 | Capstone Project | 0-0-6-8 | All previous courses |
| 8 | ELEC801 | Final Year Project | 0-0-6-8 | All previous courses |
Detailed Course Descriptions
The following are descriptions of key departmental elective courses offered in the program:
Artificial Intelligence & Machine Learning
This course introduces students to the core concepts of artificial intelligence and machine learning, emphasizing practical implementation. Students will learn algorithms such as decision trees, neural networks, clustering, regression, and reinforcement learning. The curriculum includes hands-on projects using TensorFlow and PyTorch, preparing students for real-world applications in robotics.
Computer Vision
This course focuses on image processing techniques and their application in robotics. Students will study topics like edge detection, feature extraction, object recognition, and camera calibration. Practical labs involve using OpenCV and MATLAB to build visual perception systems for robots.
Reinforcement Learning
Reinforcement learning is a branch of machine learning focused on decision-making in dynamic environments. This course covers Markov Decision Processes (MDPs), Q-learning, policy gradients, and deep reinforcement learning methods. Students will implement algorithms to train robots for autonomous navigation and task execution.
Embedded Systems
This course explores the design and development of embedded systems used in robotics. Topics include microcontrollers, real-time operating systems, interrupt handling, communication protocols (I2C, SPI), and hardware-software co-design. Students will work on building embedded systems for robotic platforms.
Control Systems
This course covers the mathematical foundations of control theory and its application to robotics. Students will learn about feedback control, stability analysis, PID controllers, state-space representation, and frequency response methods. Labs involve simulating control systems using MATLAB/Simulink and testing on physical robotic platforms.
Human-Robot Interaction
This course examines the principles of designing robots that interact effectively with humans. It covers topics such as user interface design, emotional computing, ethical considerations, and accessibility in robotics. Students will develop interactive systems for assistive and service robotics applications.
Mobile Robot Navigation
Students learn how to program mobile robots for autonomous navigation using SLAM algorithms, path planning, sensor fusion, and localization techniques. The course includes both simulation and real-world robot testing using ROS (Robot Operating System).
Swarm Intelligence
This course explores collective behavior in robotics, focusing on decentralized control and coordination among multiple robots. Students will study algorithms for swarm formation, communication protocols, and applications in agriculture, search and rescue, and environmental monitoring.
Medical Robotics
This elective focuses on the design and development of robots for healthcare settings. Topics include surgical robotics, rehabilitation robotics, telemedicine systems, and safety standards. Students will explore ethical issues related to robot-assisted healthcare and work on prototypes for assistive devices.
Industrial Automation
This course introduces students to the principles of industrial automation and robotics in manufacturing environments. It covers topics like PLC programming, SCADA systems, robotic welding, conveyor systems, and quality control processes. Students will gain practical experience with industrial robots from ABB and Siemens.
Project-Based Learning Philosophy
Mittal Institute of Technology emphasizes project-based learning as a cornerstone of the Bachelor of Robotics program. This approach ensures that students gain hands-on experience while developing critical thinking and problem-solving skills.
Mini-Projects
Mini-projects are introduced in the third semester and continue through the fourth year. These projects typically last 4-6 weeks and focus on specific aspects of robotics such as sensor integration, control algorithms, or software development. Each project is evaluated based on design documentation, implementation quality, and presentation skills.
Final-Year Thesis/Capstone Project
The final-year capstone project is a comprehensive endeavor that integrates knowledge from all previous semesters. Students form teams of 3-5 members to tackle a significant challenge in robotics. The project spans the entire semester and culminates in a public presentation and demonstration.
Project Selection Process
Students can select their projects from a list provided by faculty or propose their own ideas after consultation with mentors. The selection process ensures that projects align with academic goals, available resources, and industry relevance. Faculty mentors guide students throughout the project lifecycle, offering technical support and feedback.