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Course Outline

Introduction to AI and Robotics

  • An overview of the convergence between modern robotics and AI
  • Applications in autonomous systems, drones, and service robots
  • Core AI elements: 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 with Jupyter Notebooks

Perception and Computer Vision

  • Leveraging cameras and sensors for perception tasks
  • 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

  • Grasping the principles of probabilistic robotics
  • Applying Kalman Filters and Extended Kalman Filters (EKF)
  • Using Particle Filters in non-linear environments
  • Combining LiDAR, GPS, and IMU data for precise localization

Motion Planning and Pathfinding

  • Path planning algorithms including Dijkstra, A*, and RRT*
  • Techniques for obstacle avoidance and environment mapping
  • Real-time motion control implementation using PID
  • Optimizing dynamic paths with AI

Reinforcement Learning in Robotics

  • Core concepts of reinforcement learning
  • Designing reward-driven robotic behaviors
  • Implementing Q-learning and Deep Q-Networks (DQN)
  • Integrating RL agents into ROS for adaptive motion

Simultaneous Localization and Mapping (SLAM)

  • Understanding SLAM principles and workflows
  • Implementing SLAM using ROS packages (gmapping, hector_slam)
  • Utilizing Visual SLAM via OpenVSLAM or ORB-SLAM2
  • Testing SLAM algorithms within simulated environments

Advanced Topics and Integration

  • Speech and gesture recognition for human-robot interaction
  • Connecting with IoT and cloud robotics platforms
  • AI-driven predictive maintenance for robotic systems
  • Ethical and safety considerations in AI-enabled robotics

Capstone Project

  • Designing and simulating an intelligent mobile robot
  • Implementing navigation, perception, and motion control modules
  • Demonstrating real-time decision-making through AI models

Summary and Future Directions

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

Requirements

  • Coding proficiency in Python or C++
  • Foundational knowledge of computer science and engineering principles
  • Familiarity with probability theory, calculus, and linear algebra

Target Audience

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

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