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 Duration 21 hours

Course Outline

Introduction to AI and Robotics

  • An overview of the convergence between modern robotics and AI
  • Applications in autonomous systems, drones, and service robots
  • Core AI components: perception, planning, and control

Configuring the Development Environment

  • Setting up Python, ROS 2, OpenCV, and TensorFlow
  • Employing Gazebo or Webots for robot simulation
  • Conducting AI experiments via Jupyter Notebooks

Perception and Computer Vision

  • Utilizing cameras and sensors for environmental perception
  • Performing image classification, object detection, and segmentation with TensorFlow
  • Applying edge detection and contour tracking using OpenCV
  • Managing real-time image streaming and processing

Localization and Sensor Fusion

  • Grasping the principles of probabilistic robotics
  • Implementing Kalman Filters and Extended Kalman Filters (EKF)
  • Using Particle Filters for non-linear environments

Motion Planning and Pathfinding

  • Exploring path planning algorithms: Dijkstra, A*, and RRT*

Reinforcement Learning for Robotics

Simultaneous Localization and Mapping (SLAM)

Advanced Topics and Integration

Capstone Project

Summary and Next Steps

Requirements

  • Programming proficiency in Python or C++

Target Audience

  • Engineers
  • Robotics enthusiasts
  • Researchers in automation and AI

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