Comprehensive Curriculum Overview
The Robotics program at Electronics Service And Training Centre is structured over eight semesters to ensure a progressive and comprehensive understanding of robotics principles and applications. This curriculum integrates foundational sciences with advanced engineering concepts, preparing students for diverse roles in academia, industry, and entrepreneurship.
Semester-wise Course Structure
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| I | I | PHYS101 | Physics for Engineers | 3-1-0-4 | None |
| MATH101 | Mathematics I | 4-0-0-4 | None | ||
| CS101 | Introduction to Programming | 3-0-2-5 | None | ||
| II | PHYS102 | Electromagnetic Fields and Waves | 3-1-0-4 | PHYS101 | |
| MATH102 | Mathematics II | 4-0-0-4 | MATH101 | ||
| CS102 | Data Structures and Algorithms | 3-0-2-5 | CS101 | ||
| ME101 | Engineering Mechanics | 3-1-0-4 | PHYS101 | ||
| III | ME201 | Mechanics of Materials | 3-1-0-4 | ME101 | |
| ECE201 | Electrical Circuits and Networks | 3-1-0-4 | PHYS102 | ||
| CS201 | Object-Oriented Programming with C++ | 3-0-2-5 | CS102 | ||
| EE201 | Signals and Systems | 3-1-0-4 | MATH102 | ||
| PHYS201 | Modern Physics | 3-1-0-4 | PHYS102 | ||
| II | ME202 | Thermodynamics and Heat Transfer | 3-1-0-4 | ME201 | |
| ECE202 | Electronics Devices and Circuits | 3-1-0-4 | ECE201 | ||
| CS202 | Database Management Systems | 3-0-2-5 | CS201 | ||
| EE202 | Control Systems | 3-1-0-4 | EE201 | ||
| ME203 | Mechatronics Fundamentals | 3-1-0-4 | ME202 | ||
| PHYS202 | Optics and Quantum Physics | 3-1-0-4 | PHYS201 | ||
| CS203 | Computer Architecture | 3-0-2-5 | CS202 | ||
| III | ME301 | Robotics and Automation | 3-1-0-4 | ME203 | |
| ECE301 | Sensors and Instrumentation | 3-1-0-4 | ECE202 | ||
| CS301 | Operating Systems | 3-0-2-5 | CS203 | ||
| EE301 | Digital Signal Processing | 3-1-0-4 | EE202 | ||
| ME302 | Advanced Mechanics of Machines | 3-1-0-4 | ME202 | ||
| PHYS301 | Statistical Mechanics and Thermodynamics | 3-1-0-4 | PHYS202 | ||
| CS302 | Artificial Intelligence and Machine Learning | 3-0-2-5 | CS301 | ||
| ECE302 | Microprocessor and Microcontroller | 3-1-0-4 | ECE202 | ||
| ME303 | Design of Mechanisms and Machines | 3-1-0-4 | ME302 | ||
| EE302 | Power Electronics and Drives | 3-1-0-4 | EE202 | ||
| IV | ME401 | Robot Kinematics and Dynamics | 3-1-0-4 | ME301 | |
| ECE401 | Embedded Systems Design | 3-1-0-4 | ECE302 | ||
| CS401 | Computer Vision and Image Processing | 3-0-2-5 | CS302 | ||
| EE401 | Robot Control Systems | 3-1-0-4 | EE301 | ||
| ME402 | Advanced Manufacturing Processes | 3-1-0-4 | ME303 | ||
| ECE402 | Wireless Communication and Networks | 3-1-0-4 | ECE301 | ||
| CS402 | Natural Language Processing | 3-0-2-5 | CS302 | ||
| ME403 | Human-Robot Interaction | 3-1-0-4 | ME401 | ||
| EE402 | Reinforcement Learning in Robotics | 3-1-0-4 | CS401 | ||
| ME404 | Robotics Project Lab | 0-0-6-3 | ME401, CS401, EE401 | ||
| V | ME501 | Advanced Robotics and AI | 3-1-0-4 | ME401 | |
| ECE501 | Robotics Hardware Design | 3-1-0-4 | ECE401 | ||
| CS501 | Deep Learning for Robotics | 3-0-2-5 | CS402 | ||
| EE501 | Autonomous Navigation Systems | 3-1-0-4 | EE401 | ||
| ME502 | Robotic Manipulation and Grasping | 3-1-0-4 | ME501 | ||
| ECE502 | Smart Sensors and IoT Integration | 3-1-0-4 | ECE501 | ||
| CS502 | Robotics Ethics and Safety | 3-0-2-5 | CS501 | ||
| ME503 | Final Year Project - Robotics | 0-0-12-6 | ME404, CS501, EE501 |
Advanced Departmental Elective Courses
Departmental electives in the Robotics program are designed to deepen students' expertise in specialized areas. These courses provide advanced knowledge and practical skills necessary for tackling complex challenges in robotics.
1. Artificial Intelligence for Robotics (CS501)
This course introduces students to AI techniques specifically tailored for robotics applications. Topics include neural networks, deep learning architectures, reinforcement learning algorithms, natural language processing for robot interaction, and computer vision systems. Students gain hands-on experience in implementing AI models on robotic platforms using Python and TensorFlow.
2. Reinforcement Learning in Robotics (EE402)
Reinforcement learning is crucial for enabling robots to learn optimal behaviors through interaction with their environment. This course covers Markov Decision Processes, Q-learning, policy gradient methods, and actor-critic algorithms. Students implement RL agents using ROS and test them on simulated and real-world robotic platforms.
3. Computer Vision and Image Processing (CS401)
This elective focuses on how robots perceive and interpret visual information from cameras and sensors. It covers topics such as image enhancement, edge detection, feature extraction, object recognition, stereo vision, and real-time video processing. Students develop computer vision pipelines for robot navigation and manipulation tasks.
4. Human-Robot Interaction (ME503)
Human-robot interaction is a critical aspect of modern robotics, especially in service industries and healthcare. This course explores user-centered design principles, affective computing, gesture recognition, conversational interfaces, and ethical considerations in HRI. Students evaluate human responses to robotic systems through experiments and simulations.
5. Swarm Robotics (CS502)
This advanced topic delves into the coordination of multiple robots to achieve collective goals. It covers decentralized control strategies, consensus protocols, flocking behavior, and applications in search and rescue operations or environmental monitoring. Students simulate swarm behaviors using MATLAB and implement them on physical robot platforms.
6. Industrial Automation and Robotics (ME502)
This course bridges robotics with industrial applications, focusing on automation systems used in manufacturing plants. It covers programmable logic controllers (PLCs), robotic welding, assembly line integration, SCADA systems, and smart factory concepts. Students work on real-world projects with industry partners to optimize automation workflows.
7. Biomedical Robotics (ME501)
Biomedical robotics integrates robotics with medical technologies to improve patient care and treatment outcomes. This course covers surgical robots, prosthetic limbs, rehabilitation robotics, and medical imaging systems. Students collaborate with healthcare professionals to design robotic solutions for clinical challenges.
8. Soft Robotics and Bio-Inspired Design (ME503)
This elective explores the development of flexible, adaptable robots inspired by biological systems. It covers soft materials, compliant mechanisms, bio-inspired locomotion patterns, and applications in delicate environments. Students design and fabricate soft robotic prototypes using 3D printing and silicone molding techniques.
9. Autonomous Navigation Systems (EE501)
Autonomous navigation is essential for robots operating in unstructured environments. This course teaches path planning, SLAM algorithms, sensor fusion, GPS integration, and localization techniques. Students build autonomous robots capable of navigating complex terrains using onboard sensors and mapping technologies.
10. Robotics Ethics and Safety (CS502)
As robotics becomes more prevalent in society, ethical considerations become increasingly important. This course examines the moral implications of robot deployment, safety protocols, regulatory frameworks, and public perception of robotics technology. Students engage in debates and case studies to develop a nuanced understanding of responsible robotics development.
Project-Based Learning Philosophy
The Robotics program emphasizes project-based learning as a core component of education. This approach enables students to apply theoretical knowledge in practical settings, fostering creativity, problem-solving skills, and teamwork.
Mini-Projects
Mini-projects are integrated throughout the curriculum, starting from the second year. These projects are typically completed within one semester and involve solving specific technical problems or building functional prototypes. Students work in small teams under faculty supervision to develop solutions that demonstrate their understanding of key concepts.
Final-Year Thesis/Capstone Project
The capstone project represents the culmination of a student’s academic journey in robotics. Over two semesters, students undertake an independent research or development project under the guidance of a faculty mentor. The project must be original, technically sound, and aligned with current trends in robotics.
Project Selection and Mentorship
Students can propose their own project ideas or choose from suggested topics provided by faculty members. The selection process involves a proposal presentation followed by approval from the departmental committee. Faculty mentors are assigned based on project alignment and availability, ensuring personalized attention throughout the project lifecycle.
Evaluation Criteria
Projects are evaluated based on several criteria including technical feasibility, innovation, documentation quality, presentation skills, teamwork, adherence to timelines, and final deliverables. Students must submit progress reports, mid-term presentations, and a comprehensive final report. The evaluation process encourages continuous improvement and critical thinking.