AI for Robotics and Control Systems

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🎓 Course Modules:

🤖 Module 1: Introduction to AI in Robotics

Overview of robotics and intelligent control

Role of AI in modern robotics

Mechatronics vs. cognitive robotics

⚙️ Module 2: Fundamentals of Control Systems

Open-loop vs. closed-loop control

PID controllers and feedback systems

Stability, sensitivity, and transfer functions

🧭 Module 3: Robotic Perception & Sensor Fusion

Types of sensors (LIDAR, sonar, camera, IMU)

Sensor calibration and noise filtering

Sensor fusion techniques (Kalman Filter, Bayesian approaches)

🚦 Module 4: Path Planning and Navigation

Grid-based pathfinding (Dijkstra, A*)

Sampling-based planners (RRT, PRM)

Obstacle avoidance and dynamic environments

🧠 Module 5: Machine Learning in Robotics

Using ML for sensor data interpretation

Predictive modeling for state estimation

Integrating supervised and unsupervised learning

🔁 Module 6: Reinforcement Learning for Control

Basics of reward-driven learning

Applying Q-learning and DQN to robotic control

Simulated vs. real-world reinforcement learning

🧬 Module 7: Kinematics and Motion Control

Forward and inverse kinematics

Trajectory generation and motion constraints

Controlling wheeled and multi-joint robots

🧪 Module 8: Simulation and Testing

Using tools like Gazebo, ROS, and MATLAB/Simulink

Creating digital twins and simulation environments

Testing controllers before deployment

📡 Module 9: Real-Time Systems and Embedded AI

Programming microcontrollers and edge devices

Real-time constraints in robotic applications

Integrating AI models into embedded hardware

✅ Module 10: Final Capstone Project

Build and simulate an autonomous mobile robot

Real-time navigation, obstacle detection, and task execution

Submit project report and system design review

🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

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