Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Physical AI and Robotics
- Overview of Physical AI and its development trajectory
- Applications in industrial automation and broader sectors
- Essential components of intelligent robotic systems
Designing Robotics Systems
- Mechanical design principles for robotic applications
- Integrating sensors and actuators
- Power systems and strategies for energy efficiency
AI Models for Robotics
- Applying machine learning for perception and decision-making
- Utilizing reinforcement learning in robotic contexts
- Constructing AI pipelines for robotic systems
Real-Time Sensor Integration
- Techniques for sensor fusion
- Processing inputs from LiDAR, cameras, and various sensors
- Real-time navigation and obstacle avoidance strategies
Simulation and Testing
- Working with simulation platforms such as Gazebo and MATLAB Robotics Toolbox
- Modeling dynamic environments
- Evaluating performance and optimizing outcomes
Automation and Deployment
- Programming robots for industrial automation tasks
- Creating workflows for repetitive operations
- Ensuring 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, with a preference for 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.
