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 Duration 21 hours

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 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 conducting AI experiments

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

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

Motion Planning and Pathfinding

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

Reinforcement Learning for Robotics

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

Simultaneous Localization and Mapping (SLAM)

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

Advanced Topics and Integration

  • Incorporating speech and gesture recognition for human-robot interaction
  • Integrating with IoT and cloud robotics platforms
  • Applying 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 systems
  • Demonstrating real-time decision-making capabilities using AI models

Summary and Next Steps

  • Reviewing key AI robotics techniques
  • Exploring future trends in autonomous robotics
  • Identifying resources for continued learning

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

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