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Course Outline
Overview of Physical AI
- Defining Physical AI
- Essential elements: hardware, software, and AI integration
- Real-world applications of Physical AI in various industries
Robotics Fundamentals
- Core concepts in robotics and automation
- An introduction to sensors, actuators, and control systems
- Getting started with the Robot Operating System (ROS)
AI Algorithms for Physical Systems
- Machine learning and perception techniques for robotics
- Basics of path planning and navigation
- Introduction to autonomous decision-making and control logic
Prototyping and Constructing Intelligent Machines
- Selecting appropriate hardware: Arduino, Raspberry Pi, and alternatives
- Connecting sensors and actuators
- Assembling and testing a basic AI-driven robotic system
Practical Exercises
- Configuring a basic ROS environment
- Creating a line-following robot
- Building a basic obstacle-avoidance system
Deployment and Practical Testing
- Identifying and resolving issues in robotic systems
- Conducting field tests on prototypes
- Evaluating performance and refining the design
Challenges and Emerging Trends
- Scaling from prototypes to full-scale systems
- Ethical and safety aspects in Physical AI
- New technologies and industry innovations
Recap and Recommended Next Steps
Requirements
- Foundational programming skills (Python proficiency is advised)
- A keen interest in robotics and artificial intelligence
Target Audience
- AI developers
- Technology enthusiasts
- Students in STEM fields
14 Hours
