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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.
- Key AI components: perception, planning, and control.
Setting Up the Development Environment
- Installation of Python, ROS 2, OpenCV, and TensorFlow.
- Utilising Gazebo or Webots for robot simulation.
- Conducting AI experiments using Jupyter Notebooks.
Perception and Computer Vision
- Utilising cameras and sensors for environmental perception.
- Performing image classification, object detection, and segmentation with TensorFlow.
- Edge detection and contour tracking via OpenCV.
- Real-time image streaming and processing techniques.
Localization and Sensor Fusion
- Understanding the principles of probabilistic robotics.
- Implementing Kalman Filters and Extended Kalman Filters (EKF).
- Using Particle Filters for non-linear environments.
- Integrating LiDAR, GPS, and IMU data for precise localization.
Motion Planning and Pathfinding
- Path planning algorithms including Dijkstra, A*, and RRT*.
- Strategies for obstacle avoidance and environment mapping.
- Real-time motion control using PID.
- Dynamic path optimisation leveraging AI.
Reinforcement Learning for Robotics
- Core fundamentals of reinforcement learning.
- Designing robotic behaviors based on reward mechanisms.
- Exploration of Q-learning and Deep Q-Networks (DQN).
- Integrating RL agents into ROS for adaptive motion.
Simultaneous Localization and Mapping (SLAM)
- Understanding SLAM concepts and operational workflows.
- Implementing SLAM using ROS packages such as gmapping and hector_slam.
- Visual SLAM applications 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.
- Ethical considerations 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 techniques in AI robotics.
- Insights into future trends in autonomous robotics.
- Resources for continued learning and development.
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
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
