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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.
  • Core AI components: 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 using Jupyter Notebooks.

Perception and Computer Vision

  • Utilizing 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

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

Motion Planning and Pathfinding

  • Exploring path planning algorithms: Dijkstra, A*, and RRT*.
  • Managing obstacle avoidance and environment mapping.
  • Achieving real-time motion control via PID.
  • Dynamic path optimization driven by AI.

Reinforcement Learning for Robotics

  • 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.

Simultaneous Localization and Mapping (SLAM)

  • Grasping SLAM concepts and workflows.
  • Implementing SLAM using ROS packages (gmapping, hector_slam).
  • Utilizing Visual SLAM with OpenVSLAM or ORB-SLAM2.
  • Testing SLAM algorithms in simulated settings.

Advanced Topics and System Integration

  • Enabling human-robot interaction through speech and gesture recognition.
  • Integration with IoT and cloud robotics platforms.
  • Implementing AI-driven predictive maintenance for robots.
  • Discussing ethics 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.

Conclusion and Future Directions

  • Recap of essential AI robotics techniques.
  • Insights into future trends in autonomous robotics.
  • Resources for ongoing professional development.

Requirements

  • Proficiency in Python or C++ programming.
  • 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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