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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

Development Environment Setup

  • 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

  • Employing cameras and sensors for environmental perception
  • Image classification, object detection, and segmentation using TensorFlow
  • Edge detection and contour tracking utilizing OpenCV
  • Real-time image streaming and processing workflows

Localization and Sensor Fusion

  • Understanding the principles of probabilistic robotics
  • Kalman Filters and Extended Kalman Filters (EKF)
  • Particle Filters suited for non-linear environments
  • Data fusion from LiDAR, GPS, and IMU for accurate localization

Motion Planning and Pathfinding

  • Path planning algorithms including Dijkstra, A*, and RRT*
  • Obstacle avoidance strategies and environment mapping
  • Real-time motion control implementation using PID
  • Dynamic path optimization leveraging AI

Reinforcement Learning for Robotics

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

Simultaneous Localization and Mapping (SLAM)

  • Key concepts and workflows in SLAM
  • Implementation of SLAM using 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 enhanced human-robot interaction
  • Integration with IoT and cloud-based 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 systems
  • Demonstration of real-time decision-making using AI models

Summary and Future Directions

  • Review of essential AI robotics techniques
  • Emerging trends in autonomous robotics
  • Resources for ongoing professional development

Requirements

  • Programming proficiency in Python or C++
  • Foundational understanding of computer science and engineering principles
  • Knowledge of probability concepts, calculus, and linear algebra

Target Audience

  • Professional Engineers
  • Robotics Enthusiasts
  • Researchers specializing in automation and AI
 21 Hours

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