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Fees
₹6,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Fees
₹6,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Seats
30
Students
120
Seats
30
Students
120
The Robotics program at BAGULA MUKHI COLLEGE OF TECHNOLOGY is meticulously structured to provide students with a well-rounded education that blends theoretical knowledge with hands-on experience. The curriculum spans eight semesters and includes core engineering subjects, departmental electives, science electives, and laboratory components designed to foster innovation and practical application.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus and Analytical Geometry | 3-1-0-4 | - |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CSE101 | Introduction to Programming | 2-1-2-5 | - |
| 1 | MECH101 | Mechanics of Materials | 3-1-0-4 | - |
| 1 | EE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | LIT101 | English Communication | 2-0-0-2 | - |
| 1 | PHYS102 | Experimental Physics Lab | 0-0-3-2 | PHYS101 |
| 1 | CSE102 | Programming Lab | 0-0-3-2 | CSE101 |
| 2 | MATH201 | Linear Algebra and Differential Equations | 3-1-0-4 | MATH101 |
| 2 | PHYS201 | Electromagnetic Fields and Waves | 3-1-0-4 | PHYS101 |
| 2 | CSE201 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 2 | MECH201 | Thermodynamics and Fluid Mechanics | 3-1-0-4 | MECH101 |
| 2 | EE201 | Electronics Circuits | 3-1-0-4 | EE101 |
| 2 | MECH202 | Mechanical Design and Drafting | 2-1-0-3 | MECH101 |
| 2 | CSE202 | Database Systems | 3-1-0-4 | CSE201 |
| 2 | PHYS202 | Lab Course: Electronics Lab | 0-0-3-2 | EE201 |
| 3 | MATH301 | Numerical Methods | 3-1-0-4 | MATH201 |
| 3 | CSE301 | Object-Oriented Programming in C++ | 2-1-2-5 | CSE201 |
| 3 | MECH301 | Applied Mechanics | 3-1-0-4 | MECH201 |
| 3 | EE301 | Signals and Systems | 3-1-0-4 | EE201 |
| 3 | CSE302 | Computer Architecture | 3-1-0-4 | CSE201 |
| 3 | MECH302 | Manufacturing Processes | 3-1-0-4 | MECH201 |
| 3 | EE302 | Control Systems | 3-1-0-4 | EE201 |
| 3 | CSE303 | Operating Systems | 3-1-0-4 | CSE201 |
| 3 | MECH303 | Mechanics of Machines | 3-1-0-4 | MECH201 |
| 3 | EE303 | Digital Electronics | 3-1-0-4 | EE201 |
| 3 | LIT301 | Technical Writing and Presentation | 2-0-0-2 | - |
| 4 | CSE401 | Artificial Intelligence | 3-1-0-4 | CSE301 |
| 4 | MECH401 | Robotics Fundamentals | 3-1-0-4 | MECH301 |
| 4 | EE401 | Microprocessors and Microcontrollers | 3-1-0-4 | EE301 |
| 4 | CSE402 | Software Engineering | 3-1-0-4 | CSE301 |
| 4 | MECH402 | Sensors and Actuators | 3-1-0-4 | MECH301 |
| 4 | EE402 | Power Electronics | 3-1-0-4 | EE301 |
| 4 | CSE403 | Computer Vision | 3-1-0-4 | CSE302 |
| 4 | MECH403 | Robot Kinematics and Dynamics | 3-1-0-4 | MECH301 |
| 4 | EE403 | Embedded Systems | 3-1-0-4 | EE301 |
| 4 | CSE404 | Machine Learning | 3-1-0-4 | CSE401 |
| 4 | MECH404 | Robotic Control Systems | 3-1-0-4 | MECH301 |
| 5 | CSE501 | Reinforcement Learning | 3-1-0-4 | CSE404 |
| 5 | MECH501 | Advanced Robotics | 3-1-0-4 | MECH401 |
| 5 | EE501 | Robotics Hardware Design | 3-1-0-4 | EE401 |
| 5 | CSE502 | Natural Language Processing | 3-1-0-4 | CSE401 |
| 5 | MECH502 | Human-Robot Interaction | 3-1-0-4 | MECH401 |
| 5 | EE502 | Robotics Software Frameworks | 3-1-0-4 | EE401 |
| 5 | CSE503 | Computer Graphics and Animation | 3-1-0-4 | CSE302 |
| 5 | MECH503 | Autonomous Navigation | 3-1-0-4 | MECH401 |
| 5 | EE503 | Sensor Fusion Techniques | 3-1-0-4 | EE401 |
| 5 | CSE504 | Quantum Computing Basics | 3-1-0-4 | CSE401 |
| 6 | CSE601 | Robotics Ethics and Policy | 2-1-0-3 | - |
| 6 | MECH601 | Special Topics in Robotics | 3-1-0-4 | MECH501 |
| 6 | EE601 | Advanced Control Theory | 3-1-0-4 | EE501 |
| 6 | CSE602 | Robotics and AI Integration | 3-1-0-4 | CSE501 |
| 6 | MECH602 | Robotic Applications in Healthcare | 3-1-0-4 | MECH501 |
| 6 | EE602 | Robotic Systems Design | 3-1-0-4 | EE501 |
| 6 | CSE603 | Research Methodology | 2-1-0-3 | - |
| 6 | MECH603 | Robotic Systems Testing | 3-1-0-4 | MECH501 |
| 6 | EE603 | Power Management in Robotics | 3-1-0-4 | EE501 |
| 6 | CSE604 | Cloud Robotics | 3-1-0-4 | CSE502 |
| 7 | CSE701 | Capstone Project I | 0-0-6-6 | - |
| 7 | MECH701 | Advanced Capstone Design | 0-0-6-6 | - |
| 7 | EE701 | Final Year Project Lab | 0-0-6-6 | - |
| 7 | CSE702 | Industry Internship | 0-0-0-10 | - |
| 7 | MECH702 | Internship Practical | 0-0-0-10 | - |
| 7 | EE702 | Internship Report Writing | 0-0-0-2 | - |
| 8 | CSE801 | Capstone Project II | 0-0-6-6 | CSE701 |
| 8 | MECH801 | Final Capstone Presentation | 0-0-0-4 | MECH701 |
| 8 | EE801 | Project Defense | 0-0-0-4 | EE701 |
| 8 | CSE802 | Thesis Writing and Submission | 0-0-0-6 | - |
| 8 | MECH802 | Final Project Evaluation | 0-0-0-4 | MECH701 |
| 8 | EE802 | Final Portfolio Submission | 0-0-0-2 | - |
These advanced elective courses are designed to deepen students' understanding of specialized areas within robotics and prepare them for careers in niche fields or further research.
This course introduces students to the core concepts of AI, including search algorithms, knowledge representation, planning, and machine learning. The focus is on applying these techniques to solve real-world robotic problems, such as autonomous navigation, object recognition, and decision-making under uncertainty.
Students learn how to develop systems that can interpret visual information from the environment using cameras and other imaging devices. Topics include image processing, feature extraction, object detection, and scene understanding. Practical applications include robot vision for navigation and manipulation tasks.
This course explores how robots can learn optimal behaviors through trial and error interactions with their environment. Students study Markov Decision Processes, Q-learning, policy gradients, and deep reinforcement learning methods such as DQN and PPO. Applications include robotic control, autonomous vehicles, and game-playing agents.
This course examines how robots can interact effectively with humans in social and collaborative settings. It covers topics such as gesture recognition, voice interfaces, emotional modeling, and ethical considerations in robot design. Students also explore user experience design for robotics applications.
Students study computational approaches to processing human language for robotic communication. The course includes text classification, sentiment analysis, dialogue systems, and language generation. These skills are crucial for developing robots that can understand and respond to natural speech commands.
This course addresses the ethical implications of robotics technology and its impact on society. Students analyze issues such as job displacement, privacy concerns, autonomous weapons, and robot rights. The course also examines regulatory frameworks governing robotics development and deployment.
This course explores how cloud computing can enhance robotic capabilities by providing access to large datasets, advanced computational resources, and collaborative platforms. Students learn about distributed computing architectures, edge-cloud integration, and secure data management in robotics systems.
As quantum technologies advance, understanding their potential applications in robotics becomes increasingly important. This course introduces students to quantum mechanics, quantum algorithms, and how quantum computing might be integrated into future robotic systems for solving complex optimization problems.
This advanced course combines the principles of artificial intelligence with robotics engineering. Students learn to design hybrid systems that leverage both symbolic and subsymbolic approaches to intelligence. The course emphasizes practical implementation using modern frameworks like TensorFlow and ROS.
This flexible course allows students to explore emerging trends in robotics, such as swarm robotics, soft robotics, and bio-inspired engineering. Topics are selected based on current research and industry developments, ensuring that students stay at the forefront of technological innovation.
Our program emphasizes project-based learning as a core component of education. Students engage in both mini-projects and capstone projects throughout their academic journey, working in teams to tackle real-world challenges in robotics.
In the second year, students complete two mini-projects under faculty supervision. These projects focus on building foundational skills in system design, prototyping, and testing. Each project is evaluated based on technical execution, creativity, and teamwork.
The capstone project is the culmination of the student's learning experience. Students select a research topic or practical problem related to robotics, propose a solution, and implement it over a period of two semesters. The final project includes documentation, demonstration, and presentation before a panel of faculty members.
Faculty mentors are assigned based on the alignment between student interests and research expertise. Students are encouraged to collaborate with industry partners or academic institutions for their projects, enhancing real-world relevance and exposure.
