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Course Outline
Introduction to Physical AI and Robotics
- Overview of Physical AI and its development
- Applications in industrial automation and wider sectors
- Core components of intelligent robotic systems
Robotics System Design
- Mechanical design principles for robotic platforms
- Integration of sensors and actuators
- Power systems and energy efficiency
AI Models for Robotics
- Leveraging machine learning for perception and decision-making
- Reinforcement learning applications in robotics
- Constructing AI pipelines for robotic systems
Real-Time Sensor Integration
- Techniques for sensor fusion
- Processing data from LiDAR, cameras, and other sensing devices
- Real-time navigation and obstacle avoidance
Simulation and Testing
- Utilising simulation tools such as Gazebo and the MATLAB Robotics Toolbox
- Modelling dynamic environments
- Evaluating performance and optimising systems
Automation and Deployment
- Programming robots for industrial automation
- Creating workflows for repetitive tasks
- Ensuring safety and reliability during deployment
Advanced Topics and Future Trends
- Collaborative robots (cobots) and human-robot interaction
- Ethical and regulatory considerations in robotics
- The future trajectory of Physical AI in automation
Requirements
- Fundamental understanding of robotics and automation systems
- Competence in programming, with a preference for Python
- Basic familiarity with AI fundamentals
Target Audience
- Robotics engineers
- Automation specialists
- AI developers
21 Hours
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
