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Course Outline
Introduction to Physical AI and Robotics
- An overview of Physical AI and its historical evolution
- Applications in industrial automation and beyond
- Essential components of intelligent robotic systems
Robotics System Design
- Mechanical design principles specific to robotics
- Integration of sensors and actuators
- Power systems and strategies for energy efficiency
AI Models for Robotics
- Applying machine learning for perception and decision-making
- The role of reinforcement learning in robotics
- Constructing AI pipelines for robotic systems
Real-Time Sensor Integration
- Techniques for effective sensor fusion
- Processing inputs from LiDAR, cameras, and other sensing devices
- Real-time navigation and obstacle avoidance strategies
Simulation and Testing
- Utilizing simulation tools such as Gazebo and MATLAB Robotics Toolbox
- Modeling dynamic operational environments
- Evaluating performance and optimizing system behavior
Automation and Deployment
- Programming robots for industrial automation tasks
- Creating workflows for repetitive operations
- Safeguarding safety and reliability during deployment
Advanced Topics and Future Trends
- Collaborative robots (cobots) and human-robot interaction
- Ethical and regulatory frameworks in robotics
- The future trajectory of Physical AI in automation
Requirements
- Fundamental understanding of robotics and automation systems
- Programming proficiency, ideally in Python
- Basic familiarity with AI concepts
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.
