AI and Robotics Syllabus

The following is a representative eight-semester syllabus based on subjects commonly found in AI, Robotics, Mechatronics and related engineering curricula. It is not the official syllabus of every institution; course titles, credits, prerequisites, laboratories and semester sequencing can differ substantially.

Semester 1

• Engineering Mathematics I
• Engineering Physics
• Engineering Chemistry
• Programming Fundamentals
• Basic Electrical Engineering
• Engineering Graphics
• Communication Skills
• Physics Laboratory
• Programming Laboratory

Semester 2

• Engineering Mathematics II
• Object-Oriented Programming
• Engineering Mechanics
• Basic Electronics
• Digital Logic
• Data Structures
• Electronics Laboratory
• Workshop Practice

Semester 3

• Discrete Mathematics
• Algorithms
• Database Management Systems
• Microprocessors and Microcontrollers
• Sensors and Actuators
• Signals and Systems
• Data Structures Laboratory
• Embedded Systems Laboratory

Semester 4

• Probability and Statistics
• Operating Systems
• Computer Networks
• Control Systems
• Robot Kinematics
• Mechatronic System Design
• Control Systems Laboratory
• Robot Design Laboratory

Semester 5

• Artificial Intelligence
• Machine Learning
• Robot Dynamics
• Computer Vision
• Embedded and Real-Time Systems
• Robot Programming and Simulation
• Machine Learning Laboratory
• Robotics Laboratory

Semester 6

• Deep Learning
• Mobile Robotics
• Motion Planning
• Industrial Robotics
• Internet of Things
• Human–Robot Interaction
• Computer Vision Laboratory
• Mobile Robotics Laboratory

Semester 7

• Natural Language Processing
• Reinforcement Learning
• Autonomous Systems
• Robot Operating System
• AI Ethics and Robot Safety
• Department Elective
• Internship
• Project Phase I

Semester 8

• Multi-Robot Systems
• Intelligent Automation
• Department Elective
• Project Phase II
• Technical Seminar
• Comprehensive Viva Voce

The actual balance varies. A CSE-oriented programme may include more operating systems, databases and software engineering. A Robotics-oriented programme may contain more mechanics, manufacturing, electronics and control.

Core AI and Robotics subjects

Engineering Mathematics: Mathematics supports AI models, kinematics, dynamics, control and optimisation.

Programming: Students should learn structured programming, object-oriented design, debugging, testing and documentation.

Common languages may include:

• Python
• C
• C++
• Java
• MATLAB-style technical environments

No single language is sufficient for every robotics task.

Data Structures and Algorithms: Efficient algorithms are essential for planning, perception, mapping and software systems.

Artificial Intelligence: AI subjects may include search, planning, knowledge representation, reasoning and intelligent agents.

Machine Learning: Students learn regression, classification, clustering, feature engineering, evaluation and generalisation.

Deep Learning: Deep-learning subjects may cover neural networks, convolutional networks, sequence models and representation learning.

Computer Vision: Topics include image formation, filtering, features, object detection, tracking, depth and visual recognition.

Natural Language Processing: NLP supports language understanding, dialogue and voice-controlled systems.

Reinforcement Learning: Reinforcement Learning studies how an agent learns actions from rewards and interaction. Real-world robot learning requires careful safety and validation.

Engineering Mechanics: Mechanics provides a foundation for forces, motion and machine behaviour.

Robot Kinematics: Students study coordinate frames, transformations, forward kinematics, inverse kinematics and velocity relationships.

Robot Dynamics: Dynamics includes mass, inertia, force, torque and equations of motion.

Control Systems: Control subjects cover feedback, stability, transient response, frequency response and controller design.

Sensors: Robotic sensors may include:

• Encoders
• Cameras
• Inertial sensors
• Force sensors
• Proximity sensors
• LiDAR
• Radar
• Ultrasonic sensors
• Temperature sensors

Actuators: Actuators may include electric motors, hydraulic systems, pneumatic systems and specialised mechanisms.

Microcontrollers: Microcontrollers read sensors and control actuators in embedded systems.

Embedded Systems: Embedded subjects cover hardware-software interaction, timing, communication and resource constraints.

Real-Time Systems: Real-time systems must respond within defined timing requirements.

Robot Programming: Students learn to create software for perception, control, planning and task execution.

Robot Operating System: ROS can support messaging, hardware abstraction, simulation and modular robot software.

Motion Planning: Motion-planning algorithms find feasible paths while considering obstacles and system constraints.

Mobile Robotics: Mobile Robotics covers localisation, mapping, navigation and vehicle control.

Industrial Robotics: Students study manipulators, end effectors, programming, cell design and safety.

Human–Robot Interaction: HRI considers interfaces, trust, ergonomics, communication and collaborative behaviour.

IoT: Connected robots may exchange data with cloud, edge or industrial systems.

AI Ethics: Students should examine bias, privacy, explainability, accountability and misuse.

Robot Safety: Safety includes risk assessment, guarding, emergency stops, safe control and operational limitations.

Common electives

• Swarm Robotics
• Medical Robotics
• Surgical Robotics
• Agricultural Robotics
• Drone Technology
• Soft Robotics
• Humanoid Robotics
• Bio-Inspired Robotics
• Autonomous Vehicles
• Advanced Computer Vision
• Speech Technology
• Edge AI
• Cyber-Physical Systems
• Industrial Automation
• PLC and SCADA
• Digital Twins
• Cognitive Robotics
• Robot Learning
• Multi-Agent Systems
• Affective Computing

Representative M.Tech syllabus

Semester 1

• Mathematical Foundations
• Advanced Artificial Intelligence
• Robotics Fundamentals
• Robot Design
• Advanced Control Systems
• Robotics Laboratory
• Technical Elective

Semester 2

• Machine Learning
• Robot Sensing and Vision
• Autonomous Navigation
• Motion Planning
• Human–Robot Interaction
• Research Methodology
• Technical Elective

Semester 3

• Advanced Seminar
• Technical Elective
• Dissertation Phase I
• Research Review

Semester 4

• Dissertation Phase II
• Thesis Seminar
• Final Viva Voce

Laboratory work

A strong programme may include:

• Programming exercises
• Circuit design
• Sensor calibration
• Motor control
• Microcontroller programming
• Manipulator kinematics
• Robot-arm programming
• Mobile-robot navigation
• Computer-vision experiments
• Machine-learning model development
• ROS development
• Simulation
• PLC programming
• Human–robot interaction
• Safety testing

AI and Robotics project ideas

• Autonomous indoor delivery robot
• Vision-guided robotic arm
• Object-sorting robot
• Agricultural monitoring robot
• Warehouse navigation system
• Human-following robot
• Speech-controlled service robot
• Assistive robotic device
• Drone-based inspection system
• Robotic waste-sorting system
• Collaborative robot safety monitor
• Robot fault-detection model
• Sign-language recognition interface
• Multi-robot path-planning system
• Autonomous wheelchair prototype
• Medical supply robot
• Smart manufacturing cell
• Visual simultaneous localisation and mapping
• Reinforcement-learning simulation
• Robot digital twin

A good project should identify the task, operating environment, users, constraints, safety risks, evaluation measures and limitations.