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Duration 14 hours
Course Outline
Overview of Physical AI
- Defining the concept of Physical AI.
- Core elements: hardware infrastructure, software frameworks, and AI logic.
- Practical real-world applications of Physical AI.
Robotics Fundamentals
- Essential concepts in robotics and automation.
- An overview of critical components: sensors, actuators, and controllers.
- Getting started with the Robot Operating System (ROS).
AI Algorithms for Physical Systems
- Applying machine learning for perception in robotics.
- Fundamentals of path planning and navigation.
- Introduction to autonomous decision-making and control mechanisms.
Prototyping and Assembly of Intelligent Machines
- Selecting appropriate hardware, including Arduino, Raspberry Pi, and alternatives.
- Connecting and integrating sensors with actuators.
- Constructing and testing a basic AI-enabled robotic system.
Practical Exercises
- Configuring a foundational ROS environment.
- Programming a line-following robot.
- Developing a basic obstacle-avoidance mechanism.
Deployment and Field Testing
- Identifying and resolving issues in robotic systems.
- Conducting field tests on prototypes.
- Evaluating performance metrics and refining the design.
Future Directions and Challenges
- Scaling solutions from prototype stages to full-scale systems.
- Addressing ethical and safety considerations in Physical AI.
- Exploring emerging technologies and future innovations.
Conclusion and Future Actions
Requirements
- Foundational programming skills (Python is preferred)
- A strong interest in the fields of robotics and artificial intelligence
Target Audience
- AI software developers
- Technology enthusiasts
- STEM students
