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Duration 21 hours
Course Outline
Foundations of AI and Robotics
- Exploration 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 and configuration of Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments via Jupyter Notebooks
Perception and Computer Vision
- Leveraging cameras and sensors for effective 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
- Grasp the principles of probabilistic robotics
- Application of Kalman Filters and Extended Kalman Filters (EKF)
- Using Particle Filters for non-linear environments
- Fusing LiDAR, GPS, and IMU data for robust localization
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*
- Techniques for obstacle avoidance and environment mapping
- Real-time motion control implementation using PID
- Dynamic path optimization driven by AI
Reinforcement Learning for Robotics
- Core fundamentals of reinforcement learning
- Designing reward-based robotic behaviors
- Implementation of Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents in ROS for adaptive motion
Simultaneous Localization and Mapping (SLAM)
- Comprehension of SLAM concepts and workflows
- Deploying SLAM using ROS packages (gmapping, hector_slam)
- Implementing Visual SLAM with OpenVSLAM or ORB-SLAM2
- Validating SLAM algorithms within 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
- Ethical considerations and safety in AI-enabled robotics
Capstone Project
- Design and simulation of an intelligent mobile robot
- Implementation of navigation, perception, and motion control systems
- Demonstration of real-time decision-making using AI models
Summary and Next Steps
- Review of essential AI robotics techniques
- Emerging trends in autonomous robotics
- Resources for continued professional development
Requirements
- Programming proficiency in Python or C++
- Foundational understanding of computer science and engineering principles
- Knowledge of probability concepts, calculus, and linear algebra
Target Audience
- Engineers
- Robotics enthusiasts
- Researchers specializing in automation and AI
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 3900 € + VAT*
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Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.