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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