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Course Outline
Introduction to Physical AI and Robotics
- Overview of Physical AI concepts and their evolution
- Applications in industrial automation and broader sectors
- Essential components of intelligent robotic systems
Robotics System Design
- Mechanical design fundamentals for robotic structures
- Integrating sensors and actuators
- Power system architecture and energy efficiency strategies
AI Models for Robotics
- Applying machine learning for perception and decision-making
- Reinforcement learning techniques in robotics
- Constructing AI pipelines specifically for robotic systems
Real-Time Sensor Integration
- Advanced sensor fusion techniques
- Processing data streams from LiDAR, cameras, and other sensing hardware
- Real-time navigation and obstacle avoidance strategies
Simulation and Testing
- Utilizing simulation platforms such as Gazebo and MATLAB Robotics Toolbox
- Modeling complex dynamic environments
- Evaluating performance and executing optimization
Automation and Deployment
- Programming robots for industrial automation tasks
- Developing efficient workflows for repetitive operations
- Ensuring safety and reliability standards in deployment
Advanced Topics and Future Trends
- Collaborative robots (cobots) and human-robot interaction
- Ethical frameworks and regulatory considerations in robotics
- The future trajectory of Physical AI in automation
Requirements
- Foundational understanding of robotics and automation systems
- Strong programming proficiency, with Python preferred
- 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.