Get in Touch

Course Outline

Introduction to AI and Robotics

  • An overview of the intersection between modern robotics and AI.
  • Applications in autonomous systems, drones, and service robots.
  • Essential AI components: perception, planning, and control.

Configuring the Development Environment

  • Installation of Python, ROS 2, OpenCV, and TensorFlow.
  • Utilizing Gazebo or Webots for robot simulation.
  • Conducting AI experiments within Jupyter Notebooks.

Perception and Computer Vision

  • Employing cameras and sensors for perception tasks.
  • Performing image classification, object detection, and segmentation with TensorFlow.
  • Carrying out edge detection and contour tracking using OpenCV.
  • Managing real-time image streaming and processing.

Localization and Sensor Fusion

  • Exploring the principles of probabilistic robotics.
  • Application of Kalman Filters and Extended Kalman Filters (EKF).
  • Utilizing Particle Filters for non-linear environments.
  • Combining LiDAR, GPS, and IMU data for precise localization.

Motion Planning and Pathfinding

  • Path planning algorithms: Dijkstra, A*, and RRT*.
  • Techniques for obstacle avoidance and environment mapping.
  • Applying AI for dynamic path optimization.

Reinforcement Learning in Robotics

  • Designing robotic behaviors based on reward mechanisms.
  • Q-learning and Deep Q-Networks (DQN).
  • Integrating RL agents into ROS for adaptive motion.

Simultaneous Localization and Mapping (SLAM)

  • Comprehending SLAM concepts and operational workflows.
  • Implementing SLAM via ROS packages (gmapping, hector_slam).
  • Visual SLAM using OpenVSLAM or ORB-SLAM2.
  • Testing SLAM algorithms within simulated environments.

Advanced Topics and Integration

  • Speech and gesture recognition for human-robot interaction.
  • Integration with IoT and cloud-based robotics platforms.
  • AI-driven predictive maintenance for robotic systems.
  • Ethical considerations 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

  • Recap of key AI robotics techniques.
  • Emerging trends in autonomous robotics.
  • Resources for ongoing professional development.

Requirements

  • Proficiency in Python or C++ programming.
  • Foundational knowledge of computer science and engineering principles.
  • Working familiarity with probability concepts, calculus, and linear algebra.

Target Audience

  • Engineers.
  • Robotics enthusiasts.
  • Researchers specializing in automation and AI.
 21 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories