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Course Outline

Introduction to AI and Robotics

  • Overview of the convergence between modern robotics and AI
  • Applications in autonomous systems, drones, and service robots
  • Key AI components: perception, planning, and control

Setting Up the Development Environment

  • Installing Python, ROS 2, OpenCV, and TensorFlow
  • Utilizing Gazebo or Webots for robot simulation
  • Conducting AI experiments using Jupyter Notebooks

Perception and Computer Vision

  • Employing cameras and sensors for 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

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

Motion Planning and Pathfinding

  • Exploring path planning algorithms such as Dijkstra, A*, and RRT*
  • Implementing obstacle avoidance and environment mapping
  • Achieving real-time motion control using PID
  • Optimizing dynamic paths using AI

Reinforcement Learning for Robotics

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

Simultaneous Localization and Mapping (SLAM)

  • Comprehending SLAM concepts and workflows
  • Implementing SLAM using ROS packages (gmapping, hector_slam)
  • Performing Visual SLAM with 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
  • Implementing AI-driven predictive maintenance for robots
  • Addressing ethics 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 Next Steps

  • Review of key AI robotics techniques
  • Insights into future trends in autonomous robotics
  • Resources for continued learning

Requirements

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

Audience

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

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