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Course Outline
Introduction to AI and Robotics
- Overview of the convergence between modern robotics and AI.
- Applications in autonomous systems, drones, and service robots.
- Key AI components: perception, planning, and control.
Establishing the Development Environment
- Installation of Python, ROS 2, OpenCV, and TensorFlow.
- Utilizing Gazebo or Webots for robot simulation.
- Conducting AI experiments using Jupyter Notebooks.
Perception and Computer Vision
- Employing cameras and sensors for environmental perception.
- Performing image classification, object detection, and segmentation with TensorFlow.
- Executing edge detection and contour tracking using OpenCV.
- Managing real-time image streaming and processing.
Localization and Sensor Fusion
- Comprehending probabilistic robotics.
- Applying Kalman Filters and Extended Kalman Filters (EKF).
- Using Particle Filters for non-linear environments.
- Fusing LiDAR, GPS, and IMU data for precise localization.
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*.
- Obstacle avoidance strategies and environment mapping.
- Real-time motion control using PID.
- Dynamic path optimization driven by AI.
Reinforcement Learning for Robotics
- Fundamentals of reinforcement learning.
- Designing reward-based robotic behaviors.
- Q-learning and Deep Q-Networks (DQN).
- Integrating RL agents in ROS for adaptive motion.
Simultaneous Localization and Mapping (SLAM)
- Understanding SLAM concepts and workflows.
- Implementing SLAM with ROS packages (gmapping, hector_slam).
- Visual SLAM using OpenVSLAM or ORB-SLAM2.
- Testing SLAM algorithms in simulated environments.
Advanced Topics and Integration
- Speech and gesture recognition for human-robot interaction.
- Integration with IoT and cloud robotics platforms.
- AI-driven predictive maintenance for robots.
- Ethics and safety in AI-enabled robotics.
Capstone Project
- Designing and simulating an intelligent mobile robot.
- Implementing navigation, perception, and motion control.
- Demonstrating real-time decision-making using AI models.
Summary and Future Directions
- Review of key AI robotics techniques.
- Future trends in autonomous robotics.
- Resources for continued professional development.
Requirements
- Proficiency in Python or C++ programming.
- Foundational knowledge of computer science and engineering principles.
- Familiarity with probability concepts, calculus, and linear algebra.
Target Audience
- Engineers.
- Enthusiasts in the field of robotics.
- Researchers specializing in automation and AI.
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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its knowledge and utilization of AI for Robotics in the Future.
