Get in Touch
 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.
Investment

Price per private group, online live training, starting from 3900 € + VAT*

Contact us for an exact quote and to hear our latest promotions

Testimonials (1)

Provisional Upcoming Courses (Contact Us For More Information)

Related Categories