Physical AI

Both AGVs and AMRs are examples of Physical AI. Physical AI refers to AI systems that perform intelligent actions in the physical world, typically through robots or autonomous vehicles such as self-driving cars.

If AI helps us solve intellectual problems—such as calculating, researching, or summarizing—then Physical AI helps us tackle real-world problems, including tasks like delivering goods, driving vehicles, or lifting heavy objects.

However, without artificial intelligence, robots and autonomous vehicles cannot make decisions in situations that are not explicitly predefined.

For instance, if a robot is programmed to travel from point A to point B via route C, and an unexpected obstacle blocks that route, it requires AI to recognize the obstacle and dynamically compute an alternative path. Without AI, the robot would simply stop, unable to adapt to the new situation.

To interact with the physical world and adapt to dynamic environments, Physical AI relies on two core capabilities: perception and computation.

Perception is driven by sensors—the quality and type of hardware determine how well the system can sense its surroundings.

Computation, on the other hand, depends on algorithms that interpret sensor data and make decisions in real time.