Get in Touch

Course Outline

Introduction to AI and Robotics

  • The convergence of modern robotics and AI
  • Applications in autonomous systems, drones, and service robots
  • Core 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 environmental perception
  • Image classification, object detection, and segmentation with TensorFlow
  • Edge detection and contour tracking using OpenCV
  • Real-time image streaming and processing workflows

Localization and Sensor Fusion

  • Principles of probabilistic robotics
  • Kalman Filters and Extended Kalman Filters (EKF)
  • Particle Filters for non-linear environments
  • Localization via integration of LiDAR, GPS, and IMU data

Motion Planning and Pathfinding

  • Path planning algorithms: Dijkstra, A*, and RRT*
  • Obstacle avoidance strategies and environment mapping
  • Real-time motion control utilizing PID
  • Dynamic path optimization driven by AI

Reinforcement Learning for Robotics

  • Foundational concepts of reinforcement learning
  • Designing reward-based behaviors for robots
  • Q-learning and Deep Q-Networks (DQN)
  • Integrating RL agents in ROS for adaptive motion control

Simultaneous Localization and Mapping (SLAM)

  • Core concepts and workflows of SLAM
  • Implementing SLAM with 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 robotics platforms
  • AI-driven predictive maintenance for robotic systems
  • Ethics and safety considerations in AI-enabled robotics

Capstone Project

  • Design and simulation of an intelligent mobile robot
  • Implementation of navigation, perception, and motion control
  • Demonstrating real-time decision-making using AI models

Summary and Next Steps

  • Recap of key AI robotics techniques
  • Future trends in autonomous robotics
  • Resources for continued professional development

Requirements

  • Proficiency in programming with Python or C++
  • Fundamental knowledge of computer science and engineering principles
  • Basic familiarity with probability, calculus, and linear algebra

Target Audience

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

Testimonials (1)

Upcoming Courses

Related Categories