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Course Outline
Introduction to AI and Robotics
- An overview of the convergence of modern robotics and AI
- Applications in autonomous systems, drones, and service robots
- Essential AI components: perception, planning, and control
Configuring the Development Environment
- Setting up Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments with Jupyter Notebooks
Perception and Computer Vision
- Leveraging cameras and sensors for perception
- Performing image classification, object detection, and segmentation with TensorFlow
- Executing edge detection and contour tracking using OpenCV
- Handling real-time image streaming and processing
Localization and Sensor Fusion
- Grasping the principles of probabilistic robotics
- Applying Kalman Filters and Extended Kalman Filters (EKF)
- Utilizing Particle Filters for non-linear environments
- Fusing LiDAR, GPS, and IMU data for accurate localization
Motion Planning and Pathfinding
- Exploring path planning algorithms: Dijkstra, A*, and RRT*
- Managing obstacle avoidance and environment mapping
- Implementing real-time motion control using PID
- Optimizing dynamic paths with AI assistance
Reinforcement Learning for Robotics
- Understanding the fundamentals of reinforcement learning
- Designing reward-based robotic behaviors
- Implementing Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents in ROS for adaptive motion
Simultaneous Localization and Mapping (SLAM)
- Comprehending SLAM concepts and workflows
- Implementing SLAM with ROS packages (gmapping, hector_slam)
- Achieving Visual SLAM using OpenVSLAM or ORB-SLAM2
- Validating SLAM algorithms in simulated environments
Advanced Topics and Integration
- Speech and gesture recognition for human-robot interaction
- Integration with IoT and cloud robotics platforms
- Ethics and safety considerations in AI-enabled robotics
Capstone Project
- Designing and simulating an intelligent mobile robot
- Implementing navigation, perception, and motion control modules
- Demonstrating real-time decision-making capabilities using AI models
Summary and Next Steps
- Review of key AI robotics techniques
- Resources for continued professional development
Requirements
- Programming proficiency in Python or C++
- Foundational knowledge in probability concepts, calculus, and linear algebra
Target Audience
- Professional Engineers
- Robotics Enthusiasts
- Researchers in automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
