Comprehensive Course Listing
The following table outlines all courses offered in the Bachelor of Robotics program across eight semesters, including course codes, full titles, credit structure (L-T-P-C), and prerequisites.
| Semester | Course Code | Course Title | L-T-P-C | Prerequisites |
|---|---|---|---|---|
| I | ME101 | Engineering Graphics | 2-1-0-3 | - |
| I | PH101 | Physics for Engineers | 3-1-0-4 | - |
| I | CH101 | Chemistry for Engineers | 3-1-0-4 | - |
| I | MA101 | Calculus and Differential Equations | 4-0-0-4 | - |
| I | EE101 | Basic Electrical Circuits | 3-1-0-4 | - |
| I | CS101 | Introduction to Programming | 2-1-0-3 | - |
| I | ME102 | Mechanics of Solids | 3-1-0-4 | MA101, PH101 |
| I | PH102 | Thermodynamics | 3-1-0-4 | - |
| I | CH102 | Organic Chemistry | 3-1-0-4 | - |
| I | MA102 | Linear Algebra and Probability | 3-0-0-3 | MA101 |
| I | EE102 | Electronics Fundamentals | 3-1-0-4 | - |
| I | CS102 | Data Structures and Algorithms | 2-1-0-3 | CS101 |
| II | ME201 | Fluid Mechanics | 3-1-0-4 | PH101, MA101 |
| II | PH201 | Modern Physics | 3-1-0-4 | - |
| II | CH201 | Inorganic Chemistry | 3-1-0-4 | - |
| II | MA201 | Calculus II | 3-0-0-3 | MA101 |
| II | EE201 | Digital Electronics | 3-1-0-4 | - |
| II | CS201 | Object-Oriented Programming | 2-1-0-3 | CS101 |
| II | ME202 | Mechatronics Principles | 3-1-0-4 | ME102, EE101 |
| II | PH202 | Optics and Quantum Physics | 3-1-0-4 | - |
| II | CH202 | Physical Chemistry | 3-1-0-4 | - |
| II | MA202 | Statistics and Numerical Methods | 3-0-0-3 | MA101 |
| II | EE202 | Control Systems Fundamentals | 3-1-0-4 | - |
| II | CS202 | Database Management Systems | 2-1-0-3 | CS101 |
| III | ME301 | Dynamics of Machinery | 3-1-0-4 | ME202, MA201 |
| III | PH301 | Electromagnetic Fields | 3-1-0-4 | - |
| III | CH301 | Chemical Engineering Principles | 3-1-0-4 | - |
| III | MA301 | Transform Calculus | 3-0-0-3 | MA201 |
| III | EE301 | Signals and Systems | 3-1-0-4 | - |
| III | CS301 | Operating Systems | 2-1-0-3 | CS201 |
| III | ME302 | Machine Design | 3-1-0-4 | ME202, ME301 |
| III | PH302 | Atomic and Nuclear Physics | 3-1-0-4 | - |
| III | CH302 | Environmental Chemistry | 3-1-0-4 | - |
| III | MA302 | Complex Variables and Partial Differential Equations | 3-0-0-3 | MA201 |
| III | EE302 | Microprocessors and Microcontrollers | 3-1-0-4 | - |
| III | CS302 | Computer Networks | 2-1-0-3 | CS201 |
| IV | ME401 | Manufacturing Processes | 3-1-0-4 | ME302, ME202 |
| IV | PH401 | Optics and Lasers | 3-1-0-4 | - |
| IV | CH401 | Industrial Chemistry | 3-1-0-4 | - |
| IV | MA401 | Applied Mathematics | 3-0-0-3 | MA201 |
| IV | EE401 | Power Electronics | 3-1-0-4 | - |
| IV | CS401 | Software Engineering | 2-1-0-3 | CS201 |
| IV | ME402 | Robotics Fundamentals | 3-1-0-4 | ME301, EE301 |
| IV | PH402 | Quantum Mechanics | 3-1-0-4 | - |
| IV | CH402 | Biochemistry | 3-1-0-4 | - |
| IV | MA402 | Mathematical Modeling | 3-0-0-3 | MA301 |
| IV | EE402 | Communication Systems | 3-1-0-4 | - |
| IV | CS402 | Artificial Intelligence | 2-1-0-3 | CS301 |
| V | ME501 | Advanced Robotics | 3-1-0-4 | ME402, EE301 |
| V | PH501 | Condensed Matter Physics | 3-1-0-4 | - |
| V | CH501 | Pharmaceutical Chemistry | 3-1-0-4 | - |
| V | MA501 | Linear Programming | 3-0-0-3 | MA302 |
| V | EE501 | Electromagnetic Interference and Compatibility | 3-1-0-4 | - |
| V | CS501 | Machine Learning | 2-1-0-3 | CS401 |
| V | ME502 | Sensor Technology | 3-1-0-4 | ME402, EE301 |
| V | PH502 | Relativity and Cosmology | 3-1-0-4 | - |
| V | CH502 | Medicinal Chemistry | 3-1-0-4 | - |
| V | MA502 | Stochastic Processes | 3-0-0-3 | MA401 |
| V | EE502 | Embedded Systems Design | 3-1-0-4 | - |
| V | CS502 | Computer Vision | 2-1-0-3 | CS401 |
| VI | ME601 | Autonomous Navigation | 3-1-0-4 | ME501, EE501 |
| VI | PH601 | Nuclear Physics and Applications | 3-1-0-4 | - |
| VI | CH601 | Industrial Biotechnology | 3-1-0-4 | - |
| VI | MA601 | Advanced Numerical Methods | 3-0-0-3 | MA501 |
| VI | EE601 | Robot Control Systems | 3-1-0-4 | - |
| VI | CS601 | Deep Learning | 2-1-0-3 | CS501 |
| VI | ME602 | Human-Robot Interaction | 3-1-0-4 | ME502, CS502 |
| VI | PH602 | Quantum Computing | 3-1-0-4 | - |
| VI | CH602 | Food Chemistry | 3-1-0-4 | - |
| VI | MA602 | Mathematical Optimization | 3-0-0-3 | MA501 |
| VI | EE602 | Power Systems | 3-1-0-4 | - |
| VI | CS602 | Natural Language Processing | 2-1-0-3 | CS501 |
| VII | ME701 | Swarm Robotics | 3-1-0-4 | ME601, CS601 |
| VII | PH701 | Advanced Optics | 3-1-0-4 | - |
| VII | CH701 | Environmental Monitoring | 3-1-0-4 | - |
| VII | MA701 | Advanced Probability Theory | 3-0-0-3 | MA601 |
| VII | EE701 | Signal Processing | 3-1-0-4 | - |
| VII | CS701 | Reinforcement Learning | 2-1-0-3 | CS601 |
| VII | ME702 | Biomedical Robotics | 3-1-0-4 | ME602, PH501 |
| VII | PH702 | Gravitational Waves | 3-1-0-4 | - |
| VII | CH702 | Pharmaceutical Analysis | 3-1-0-4 | - |
| VII | MA702 | Applied Statistics | 3-0-0-3 | MA601 |
| VII | EE702 | Antenna Theory | 3-1-0-4 | - |
| VII | CS702 | Computational Intelligence | 2-1-0-3 | CS601 |
| VIII | ME801 | Capstone Project | 4-0-0-4 | ME701, CS701 |
| VIII | PH801 | Advanced Quantum Mechanics | 3-1-0-4 | - |
| VIII | CH801 | Industrial Waste Management | 3-1-0-4 | - |
| VIII | MA801 | Mathematical Modeling in Robotics | 3-0-0-3 | MA701 |
| VIII | EE801 | Robotics and Automation | 3-1-0-4 | - |
| VIII | CS801 | Advanced AI Applications | 2-1-0-3 | CS701 |
| VIII | ME802 | Robotics Thesis | 4-0-0-4 | ME801, CS801 |
| VIII | PH802 | Advanced Electromagnetic Fields | 3-1-0-4 | - |
| VIII | CH802 | Food Processing Technology | 3-1-0-4 | - |
| VIII | MA802 | Statistical Inference | 3-0-0-3 | MA701 |
| VIII | EE802 | Power Electronics for Robotics | 3-1-0-4 | - |
| VIII | CS802 | Robotics Software Architecture | 2-1-0-3 | CS701 |
Advanced Departmental Electives
The advanced departmental elective courses in the Bachelor of Robotics program are designed to provide students with specialized knowledge and practical skills in specific areas of robotics engineering. These courses offer in-depth exploration of topics relevant to current industry trends and research directions.
One such course is 'Autonomous Navigation', which delves into the algorithms and techniques used for enabling robots to navigate complex environments autonomously. Students learn about SLAM (Simultaneous Localization and Mapping), path planning, obstacle avoidance, and sensor fusion methods. The course includes hands-on lab sessions where students implement navigation algorithms on robotic platforms.
Another key elective is 'Human-Robot Interaction', which explores the design and implementation of robots that can effectively communicate and collaborate with humans in various settings. Topics include social robotics, user interface design, ethical considerations, and psychological aspects of human-robot relationships. Students engage in projects involving assistive devices for people with disabilities and interactive entertainment systems.
'Biomedical Robotics' is a specialized track that combines robotics with healthcare applications. Students study topics such as surgical robotics, prosthetics, rehabilitation robotics, and biomedical device design. The program includes collaborations with medical institutions and hospitals for clinical research projects, providing students with real-world exposure to healthcare challenges and solutions.
'Robotics in Agriculture' addresses the growing need for automation in agriculture through the development of robots for crop monitoring, harvesting, and pest control. Students learn about precision farming techniques, sensor technologies, and sustainable practices. Projects often involve working with local farmers and agricultural cooperatives to implement real-world solutions.
The 'Space Robotics' course prepares students for careers in aerospace and space exploration by focusing on robotics applications in space missions. Topics include spacecraft design, orbital mechanics, remote sensing, and planetary rover systems. Students collaborate with space agencies and aerospace companies on research projects related to Mars rovers and satellite deployment.
'Robotics and AI' emphasizes the integration of artificial intelligence with robotics systems. Students study machine learning algorithms, neural networks, computer vision, and natural language processing as applied to robotics. Projects often involve developing intelligent agents that can learn and adapt in real-time environments.
'Robotics for Environmental Applications' is an emerging field that focuses on using robotics for environmental monitoring and remediation. Students explore topics such as drone-based pollution detection, underwater robots for oceanographic research, and autonomous systems for climate change mitigation. The program includes partnerships with environmental NGOs and government agencies.
Project-Based Learning Philosophy
The Department of Robotics at Gyan Ganga College of Technology places a strong emphasis on project-based learning as a core component of its educational philosophy. This approach ensures that students gain practical experience and develop critical thinking skills necessary for success in the field of robotics.
The structure of project-based learning begins with mini-projects in the second year, which are designed to reinforce theoretical concepts through hands-on experimentation. These projects typically last for one semester and involve working in small teams to design, build, and test robotic systems or software applications related to course content.
As students progress to the third year, they engage in more complex projects that require interdisciplinary collaboration and integration of multiple skills. These projects often involve solving real-world problems with innovative solutions, providing students with exposure to industry challenges and expectations.
The final-year capstone project represents the culmination of the program's learning objectives. Students are required to select a project topic under the guidance of a faculty mentor and work on it for an entire semester. The project must demonstrate advanced technical capabilities, creativity, and innovation in robotics engineering.
Evaluation criteria for projects include technical execution, innovation, presentation skills, teamwork, and adherence to project timelines. Students are assessed not only on their final deliverables but also on their ability to manage the project lifecycle effectively, from initial concept development to implementation and documentation.
The department maintains a robust system for selecting project topics, ensuring that they align with current industry trends and research directions. Faculty mentors play a crucial role in guiding students through the process, helping them refine their ideas, overcome technical challenges, and achieve successful outcomes.