Robotics Fundamentals with AI

This program focuses on the integration of AI with robotics, highlighting both hardware and software aspects. Participants will learn about robot kinematics, sensors, actuators, and AI algorithms that enable autonomous navigation, object detection, and manipulation. The course provides hands-on experience in programming robots using AI-driven techniques.

• Understand the fundamentals of robotics and their components.
• Learn to integrate AI into robotic systems for perception and decision-making.
• Develop and implement motion planning and navigation algorithms.
• Gain hands-on experience in building and programming AI-powered robots.
• Explore the applications of reinforcement learning in autonomous robotics.

1. Introduction to Robotics and AI
   • Overview of Robotics: History, Trends, and Applications
   • Introduction to AI in Robotics: Role of AI in Sensing, Planning, and Control
   • Key Areas of Robotics (Mobile Robots, Industrial Robots, Humanoids)
2. Robot Kinematics and Dynamics
   • Forward and Inverse Kinematics
   • Robot Manipulation and Arm Movements
   • Dynamics of Robotic Systems
3. Perception and Sensors in Robotics
   • Types of Sensors in Robotics (LIDAR, Cameras, Proximity Sensors, IMUs)
   • Sensor Fusion: Combining Multiple Sources of Data
   • AI for Robotic Perception: Visual Recognition, Depth Sensing
4. Path Planning and Motion Control
   • Basic Path Planning Algorithms (Dijkstra, A*)
   • AI-Based Path Planning (Probabilistic Roadmap, RRT, Reinforcement Learning)
   • Motion Control Techniques: PID Control, Trajectory Planning
5. Computer Vision in Robotics
   • Basics of Computer Vision for Robots
   • AI for Object Detection and Recognition
   • Real-Time Image Processing for Robotic Applications (e.g., SLAM)
6. Localization and Mapping
   • Simultaneous Localization and Mapping (SLAM)
   • AI Techniques for SLAM (EKF, Particle Filters, Visual SLAM)
   • Applications of SLAM in Autonomous Vehicles and Drones
7. Robotic Manipulation and Grasping
   • AI for Robotic Grasping and Manipulation
   • Robot Hands and End Effectors
   • Grasp Planning and Force Sensing
8. Robotics Operating Systems (ROS)
   • Introduction to ROS and Its Architecture
   • Building and Simulating Robots in ROS
   • AI Integration in ROS-Based Systems
9. Reinforcement Learning for Robotics
   • Basics of Reinforcement Learning (Q-Learning, Policy Gradients)
   • Applying Reinforcement Learning to Robotics Tasks
   • Case Studies: Robotic Arm Control, Autonomous Drone Navigation
10. Human-Robot Interaction (HRI)
    • Designing Robots for Human Interaction
    • AI for Speech Recognition and Natural Language Understanding in Robots
    • Ethical Considerations in Human-Robot Collaboration
11. Autonomous Robots and AI
    • AI for Autonomous Navigation in Mobile Robots
    • Sensor Fusion and AI for Real-Time Decision Making
    • Case Studies: Autonomous Vehicles, Drones, Warehouse Robots