Curriculum Overview
The Bachelor of Robotics program at Patel College is meticulously structured across eight semesters, with each semester designed to build upon the previous one. The curriculum balances foundational science and mathematics with advanced engineering concepts and specialized robotics applications.
Course Structure by Semester
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ME101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | PH101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CE101 | Introduction to Programming | 3-1-0-4 | None |
| 1 | EE101 | Basic Electronics and Circuits | 3-1-0-4 | None |
| 1 | ME102 | Introduction to Robotics Lab | 0-0-3-2 | None |
| 2 | ME103 | Engineering Mathematics II | 3-1-0-4 | ME101 |
| 2 | PH102 | Modern Physics and Quantum Mechanics | 3-1-0-4 | PH101 |
| 2 | CS101 | Data Structures and Algorithms | 3-1-0-4 | CE101 |
| 2 | EE102 | Electrical Circuits and Machines | 3-1-0-4 | EE101 |
| 2 | ME104 | Robotics Fundamentals Lab | 0-0-3-2 | ME102 |
| 3 | ME201 | Control Systems | 3-1-0-4 | ME103 |
| 3 | CS201 | Computer Programming for Robotics | 3-1-0-4 | CS101 |
| 3 | EE201 | Digital Electronics and Logic Design | 3-1-0-4 | EE102 |
| 3 | ME202 | Sensors and Actuators | 3-1-0-4 | EE102 |
| 3 | ME203 | Embedded Systems Design Lab | 0-0-3-2 | CS101, ME104 |
| 4 | ME301 | Signal Processing for Robotics | 3-1-0-4 | ME201 |
| 4 | CS301 | Machine Learning Fundamentals | 3-1-0-4 | CS201 |
| 4 | EE301 | Microcontroller Programming | 3-1-0-4 | EE201 |
| 4 | ME302 | Robotics Software Architecture Lab | 0-0-3-2 | ME203 |
| 5 | ME401 | Artificial Intelligence for Robotics | 3-1-0-4 | CS301 |
| 5 | CS401 | Computer Vision and Image Processing | 3-1-0-4 | CS301 |
| 5 | ME402 | Human-Robot Interaction | 3-1-0-4 | ME201 |
| 5 | ME403 | Advanced Robotics Lab | 0-0-3-2 | ME302 |
| 6 | ME501 | Autonomous Navigation Systems | 3-1-0-4 | ME401 |
| 6 | CS501 | Deep Learning for Robotics | 3-1-0-4 | CS401 |
| 6 | ME502 | Swarm Robotics and Distributed Control | 3-1-0-4 | ME402 |
| 6 | ME503 | Robotics Project Development Lab | 0-0-3-2 | ME403 |
| 7 | ME601 | Research Methodology in Robotics | 3-1-0-4 | ME501 |
| 7 | CS601 | Natural Language Processing for Robots | 3-1-0-4 | CS501 |
| 7 | ME602 | Robotics in Healthcare Applications | 3-1-0-4 | ME502 |
| 7 | ME603 | Final Year Capstone Project | 0-0-6-6 | ME503 |
| 8 | ME701 | Industry Internship and Thesis Writing | 0-0-0-6 | ME603 |
Detailed Elective Course Descriptions
Advanced elective courses in the Bachelor of Robotics program are designed to provide specialized knowledge and skills aligned with emerging trends in robotics.
Machine Learning for Robotics
This course explores how machine learning techniques can be applied to solve complex problems in robotics. Students learn about supervised, unsupervised, and reinforcement learning algorithms tailored for robotic applications. Practical assignments involve training robots to perform tasks like object recognition, path planning, and adaptive control.
Computer Vision and Image Processing
This course delves into the principles of computer vision and image processing techniques used in robotics. Topics include feature extraction, object detection, stereo matching, and neural network architectures for visual perception. Students implement projects using OpenCV and TensorFlow libraries to build visual navigation systems.
Human-Robot Interaction (HRI)
This course focuses on designing effective interfaces between humans and robots. It covers topics such as non-verbal communication, emotional expression in robots, and ethical considerations in robot deployment. Practical components include user studies and prototyping interactive robotic systems.
Autonomous Navigation Systems
Students learn about various navigation methods used in autonomous robots, including SLAM (Simultaneous Localization and Mapping), GPS integration, and sensor fusion. The course combines theoretical concepts with hands-on lab work involving robot simulation environments like Gazebo.
Deep Learning for Robotics
This advanced elective introduces students to deep learning models specifically adapted for robotics tasks. It covers convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer architectures for robot perception, manipulation, and decision-making. Projects involve real-time implementation on robot platforms.
Swarm Robotics and Distributed Control
This course explores collective behavior in robot swarms and distributed control strategies. Students study decentralized algorithms, consensus protocols, and multi-agent systems used in search-and-rescue missions or environmental monitoring. Simulations and physical swarm experiments are conducted using ROS (Robot Operating System).
Robotics in Healthcare Applications
This elective focuses on the use of robotics in medical settings. It covers topics like surgical robots, prosthetic limbs, rehabilitation devices, and telepresence systems. Case studies include real-world implementations at hospitals and research centers.
Bio-Inspired Robotics
Students explore how nature inspires engineering solutions in robotics. This includes biomimetic designs for locomotion, sensing, and communication in robots. Projects involve building robots that mimic animal behaviors such as crawling, flying, or swimming.
Industrial Automation and Manufacturing Robotics
This course teaches students how to integrate robotics into manufacturing processes. Topics include PLC programming, robot simulation, safety standards, and automation systems. Practical sessions involve working with industrial robots like ABB and Fanuc models.
Mobile Robotics and Drones
Students learn the principles of designing and controlling mobile robots, including wheeled, legged, and aerial drones. The course covers navigation, localization, obstacle avoidance, and mission planning for autonomous vehicles. Projects include building and testing drone prototypes.
Project-Based Learning Philosophy
The Department of Robotics at Patel College emphasizes project-based learning as the core pedagogical approach. From the first year, students engage in mini-projects that build foundational skills. These projects progress in complexity over time, culminating in a capstone project in the final year.
Mini-Projects
Mini-projects are assigned every semester to reinforce theoretical knowledge with practical application. They typically last 6-8 weeks and involve small groups of 3-5 students. Projects are selected based on student interests, faculty expertise, and industry relevance.
Final Year Capstone Project
The capstone project is a major component of the program, requiring students to apply all learned concepts in solving a real-world problem. Students form teams of 3-5 members and work closely with a faculty mentor. The project involves research, design, implementation, testing, and documentation phases.
Project Selection and Mentorship
Students select their projects from a list provided by faculty mentors or propose their own ideas after discussion with advisors. Each project is evaluated based on feasibility, innovation, technical depth, and contribution to the field. Mentors guide students through each phase of the project lifecycle.