Curriculum

The curriculum at LAXMIPATI INSTITUTE OE SCIENCE AND TECHNOLOGY BHOPAL is meticulously designed to provide a comprehensive understanding of robotics through a structured progression of core courses, departmental electives, and hands-on laboratory experiences.

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
I	MA101	Mathematics I	3-1-0-4	-
I	PH101	Physics for Engineers	3-1-0-4	-
I	CS101	Introduction to Programming	2-0-2-4	-
I	EE101	Basic Electrical Engineering	3-1-0-4	-
I	ME101	Engineering Mechanics	3-1-0-4	-
I	CE101	Introduction to Civil Engineering	3-1-0-4	-
I	CH101	Chemistry for Engineers	3-1-0-4	-
I	HS101	Communication Skills	2-0-0-2	-
I	GE101	General Engineering	2-0-0-2	-
II	MA201	Mathematics II	3-1-0-4	MA101
II	PH201	Modern Physics	3-1-0-4	PH101
II	CS201	Data Structures and Algorithms	3-1-0-4	CS101
II	EE201	Circuit Analysis	3-1-0-4	EE101
II	ME201	Mechanics of Materials	3-1-0-4	ME101
II	CE201	Structural Analysis	3-1-0-4	CE101
II	CH201	Organic Chemistry	3-1-0-4	CH101
II	HS201	English for Technical Communication	2-0-0-2	HS101
II	GE201	Engineering Ethics	2-0-0-2	-
III	MA301	Mathematics III	3-1-0-4	MA201
III	PH301	Electromagnetic Fields	3-1-0-4	PH201
III	CS301	Object-Oriented Programming	3-1-0-4	CS201
III	EE301	Electronics Devices and Circuits	3-1-0-4	EE201
III	ME301	Thermodynamics	3-1-0-4	ME201
III	CE301	Soil Mechanics	3-1-0-4	CE201
III	CH301	Inorganic Chemistry	3-1-0-4	CH201
III	HS301	Technical Writing	2-0-0-2	HS201
III	GE301	Project Management	2-0-0-2	-
IV	MA401	Mathematics IV	3-1-0-4	MA301
IV	PH401	Quantum Mechanics	3-1-0-4	PH301
IV	CS401	Database Systems	3-1-0-4	CS301
IV	EE401	Control Systems	3-1-0-4	EE301
IV	ME401	Mechanics of Machines	3-1-0-4	ME301
IV	CE401	Hydraulics and Pneumatics	3-1-0-4	CE301
IV	CH401	Physical Chemistry	3-1-0-4	CH301
IV	HS401	Presentation Skills	2-0-0-2	HS301
IV	GE401	Entrepreneurship	2-0-0-2	-
V	MA501	Mathematics V	3-1-0-4	MA401
V	PH501	Optics and Laser Physics	3-1-0-4	PH401
V	CS501	Operating Systems	3-1-0-4	CS401
V	EE501	Signal and Systems	3-1-0-4	EE401
V	ME501	Robotics Fundamentals	3-1-0-4	ME401
V	CE501	Geotechnical Engineering	3-1-0-4	CE401
V	CH501	Chemical Kinetics	3-1-0-4	CH401
V	HS501	Leadership and Teamwork	2-0-0-2	HS401
V	GE501	Sustainable Engineering	2-0-0-2	-
VI	MA601	Mathematics VI	3-1-0-4	MA501
VI	PH601	Atomic and Nuclear Physics	3-1-0-4	PH501
VI	CS601	Computer Networks	3-1-0-4	CS501
VI	EE601	Power Electronics	3-1-0-4	EE501
VI	ME601	Advanced Robotics	3-1-0-4	ME501
VI	CE601	Structural Dynamics	3-1-0-4	CE501
VI	CH601	Industrial Chemistry	3-1-0-4	CH501
VI	HS601	Research Methodology	2-0-0-2	HS501
VI	GE601	Global Engineering	2-0-0-2	-
VII	MA701	Mathematics VII	3-1-0-4	MA601
VII	PH701	Quantum Field Theory	3-1-0-4	PH601
VII	CS701	Artificial Intelligence	3-1-0-4	CS601
VII	EE701	Microcontroller Applications	3-1-0-4	EE601
VII	ME701	Mobile Robotics	3-1-0-4	ME601
VII	CE701	Geotechnical Engineering II	3-1-0-4	CE601
VII	CH701	Biochemistry	3-1-0-4	CH601
VII	HS701	Project Proposal Writing	2-0-0-2	HS601
VII	GE701	Engineering Economics	2-0-0-2	-
VIII	MA801	Mathematics VIII	3-1-0-4	MA701
VIII	PH801	Statistical Physics	3-1-0-4	PH701
VIII	CS801	Machine Learning	3-1-0-4	CS701
VIII	EE801	Advanced Control Systems	3-1-0-4	EE701
VIII	ME801	Human-Robot Interaction	3-1-0-4	ME701
VIII	CE801	Structural Health Monitoring	3-1-0-4	CE701
VIII	CH801	Environmental Chemistry	3-1-0-4	CH701
VIII	HS801	Final Project Development	2-0-0-2	HS701
VIII	GE801	Final Project Presentation	2-0-0-2	-

Advanced Departmental Electives

Artificial Intelligence for Robotics (CS701): This course introduces students to the application of AI techniques in robotics, including neural networks, deep learning, reinforcement learning, and natural language processing. Students will learn how to implement intelligent behaviors in robots using modern AI frameworks.

Advanced Control Systems (EE801): Building upon earlier control theory courses, this subject covers advanced topics such as nonlinear control, robust control, adaptive control, and optimal control. Students will explore practical applications of these concepts in robotics and automation systems.

Human-Robot Interaction (ME801): This course explores the design and implementation of systems that enable effective communication between humans and robots. It covers topics such as speech recognition, gesture interpretation, affective computing, and social robotics.

Mobile Robotics (ME701): Students will learn about navigation, path planning, localization, and dynamic obstacle avoidance in mobile robotic systems. The course includes both theoretical concepts and practical implementation using ROS (Robot Operating System).

Embedded Systems for Robotics (EE701): This course focuses on designing and programming embedded systems for robotics applications. Students will gain hands-on experience with microcontrollers, real-time operating systems, and hardware-software integration.

Computer Vision in Robotics (CS702): Students will study techniques for processing and analyzing visual data in robotic systems. Topics include image filtering, feature extraction, object detection, and scene reconstruction using computer vision algorithms.

Reinforcement Learning for Autonomous Agents (CS801): This advanced course delves into reinforcement learning methodologies applied to autonomous agents in robotics. Students will implement and evaluate various RL algorithms including Q-learning, policy gradients, and actor-critic methods.

Swarm Robotics (ME601): This subject explores collective behavior in groups of robots, enabling them to perform tasks cooperatively without centralized control. Topics include coordination algorithms, distributed computing, and scalable robotics systems.

Bio-Inspired Robotics (ME501): Drawing inspiration from nature, students will learn how to develop robots that mimic biological systems. This includes biomimetic locomotion, soft robotics, and bio-inspired sensors and actuators.

Industrial Robotics (ME602): This course focuses on automation in manufacturing processes, including robot programming, safety standards, and integration with existing industrial infrastructure. Students will gain practical experience through lab sessions and industry visits.

Project-Based Learning Philosophy

The department believes that project-based learning is essential for developing competent and innovative robotics engineers. Students are encouraged to work on projects throughout their academic journey, starting from small group assignments in early semesters to large-scale capstone projects in the final year.

Mini-projects typically last 4-6 weeks and involve solving specific engineering problems using fundamental concepts learned in core courses. These projects allow students to apply theoretical knowledge in practical settings and develop problem-solving skills.

The final-year thesis or capstone project is a significant component of the program, lasting approximately 12 weeks. Students work closely with faculty mentors to design, implement, and document a substantial robotics system or algorithm. The evaluation criteria include technical depth, innovation, presentation quality, and overall impact on the field.

Project selection involves an open call process where students submit proposals based on their interests and available resources. Faculty members provide guidance in selecting appropriate topics and ensuring feasibility within the given timeframe.