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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
  • Essential AI components: perception, planning, and control

Setting Up the Development Environment

  • Installation of Python, ROS 2, OpenCV, and TensorFlow
  • Utilizing Gazebo or Webots for robot simulation
  • Employing Jupyter Notebooks for AI experimentation

Perception and Computer Vision

  • Leveraging cameras and sensors for environmental 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

  • Grasping the principles of probabilistic robotics
  • Implementing Kalman Filters and Extended Kalman Filters (EKF)
  • Applying Particle Filters for non-linear environments
  • Fusing LiDAR, GPS, and IMU data for accurate localization

Motion Planning and Pathfinding

  • Path planning algorithms: Dijkstra, A*, and RRT*
  • Strategies for obstacle avoidance and environment mapping
  • Real-time motion control using PID methods
  • Dynamic path optimization enabled by AI

Reinforcement Learning for Robotics

  • Core fundamentals of reinforcement learning
  • Designing robotic behaviors based on reward mechanisms
  • Exploring Q-learning and Deep Q-Networks (DQN)
  • Integrating RL agents within ROS for adaptive motion

Simultaneous Localization and Mapping (SLAM)

  • Understanding SLAM concepts and operational workflows
  • Implementing SLAM using ROS packages (gmapping, hector_slam)
  • Visual SLAM applications using OpenVSLAM or ORB-SLAM2
  • Testing SLAM algorithms in simulated settings

Advanced Topics and Integration

  • Speech and gesture recognition for enhanced human-robot interaction
  • Integration with IoT and cloud 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 modules
  • Demonstrating real-time decision-making using AI models

Summary and Next Steps

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

Requirements

  • Programming proficiency in Python or C++
  • A foundational understanding of computer science and engineering principles
  • Familiarity with probability concepts, calculus, and linear algebra

Target Audience

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

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