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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
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.