Semester-wise syllabus for an M.Tech in Robotics

 

Semester 1: Core Foundations

Courses: 

1. Robotics Mechanics and Kinematics

   - Rigid body motion, forward/inverse kinematics, Jacobians, and trajectory planning. 

2. Robot Dynamics and Control 

   - Lagrangian mechanics, PID control, state-space control, and torque-based control. 

3. Sensors and Actuators in Robotics 

   - IMUs, LiDAR, encoders, stepper/servo motors, and pneumatic systems. 

4. Artificial Intelligence for Robotics 

   - Path planning (A*, RRT), SLAM (Simultaneous Localization and Mapping), and reinforcement learning. 

5. Research Methodology

   - Technical writing, data analysis (Python/MATLAB), and experimental design. 

 

Labs:

- Robotics Simulation Lab (ROS, Gazebo, Webots) 

- Embedded Systems Lab (Arduino, Raspberry Pi, and motor control) 

 

Semester 2: Specialization & Electives 

Core Courses: 

1. Computer Vision for Robotics 

   - Feature detection, object recognition, OpenCV, and deep learning (CNN, YOLO). 

2. Human-Robot Interaction (HRI) 

   - Gesture recognition, natural language processing (NLP), and ethical considerations. 

 

Electives (Examples): 

- Autonomous Vehicles (perception, control, and sensor fusion) 

- Medical Robotics (surgical robots, prosthetics, and exoskeletons) 

- Industrial Robotics (PLC programming, collaborative robots/Cobots) 

- Soft Robotics (materials, bio-inspired design, and compliant mechanisms) 

- Swarm Robotics (multi-agent systems, consensus algorithms) 

Labs: 

- Computer Vision Lab (OpenCV, PyTorch/TensorFlow) 

- Industrial Robotics Lab (UR5, ABB, or Fanuc robot programming) 

 

Semester 3:

Advanced Electives & Project Work 

Electives (Examples): 

- AI/ML in Robotics (deep reinforcement learning, imitation learning) 

- Robotics in Space Exploration (rover design, orbital mechanics) 

- Bio-inspired Robotics (hexapods, drone swarms, underwater robots) 

- Robot Operating System (ROS) Advanced (ROS 2, navigation stack, MoveIt) 

- Ethics and Safety in Robotics (AI ethics, fail-safe mechanisms) 

 

Project/Dissertation:

- Phase 1: Topic selection (e.g., autonomous drone navigation, robotic arm control, AI-driven HRI), literature review, and proposal. 

- Seminars: Presentations on trends like edge AI for robots, quantum robotics, or ethical AI frameworks. 

 

Semester 4: Thesis/Project Completion 

Thesis/Project: 

- Full-time focus on hardware/software integration (e.g., building a robotic prototype, deploying AI models on edge devices). 

- Final documentation, viva voce defense, and potential collaboration with industries/research labs. 

Additional Components: 

- Industrial Internship (optional, with robotics firms like Boston Dynamics, ABB, or startups). 

- Workshops: Training in tools like SolidWorks (CAD), V-REP/CoppeliaSim, or ROS 2. 

 

Elective Tracks (Specializations):

1. Autonomous Systems 

   - Self-driving cars, drones, and AI-based navigation. 

2. Medical Robotics

   - Surgical automation, rehabilitation robotics, and nanorobotics. 

3. Industrial Automation

   - Collaborative robots, digital twins, and Industry 4.0 integration. 

4. AI-Driven Robotics 

   - Reinforcement learning, neural networks for perception/control.