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Duration 21 hours
Course Outline
Foundations of Physical AI and Robotics
- Overview of Physical AI concepts and their historical evolution
- Applications extending beyond industrial automation
- Essential components of intelligent robotic architectures
Robotics System Architecture
- Mechanical design principles applied to robotics
- Integration strategies for sensors and actuators
- Power management and energy efficiency considerations
AI Models for Robotic Intelligence
- Leveraging machine learning for perception and decision logic
- Application of reinforcement learning in robotic contexts
- Constructing robust AI pipelines for robotic systems
Real-Time Sensor Integration
- Advanced sensor fusion methodologies
- Processing inputs from LiDAR, cameras, and other sensing devices
- Implementing real-time navigation and obstacle avoidance strategies
Simulation and Validation
- Utilizing simulation platforms such as Gazebo and the MATLAB Robotics Toolbox
- Modeling complex and dynamic environmental conditions
- Evaluating performance and optimizing system efficiency
Automation and Deployment Strategies
- Programming robots for industrial automation tasks
- Creating efficient workflows for repetitive processes
- Safeguarding safety and reliability during deployment
Advanced Topics and Emerging Trends
- Collaborative robots (cobots) and human-robot interaction dynamics
- Ethical frameworks and regulatory considerations in robotics
- Future trajectories of Physical AI in automation
Requirements
- Fundamental understanding of robotics and automation systems
- Strong programming proficiency, particularly in Python
- Basic knowledge of AI fundamentals
Target Audience
- Robotics engineers
- Automation specialists
- AI developers
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.