AI on board: decisions in the robot itself

Intelligence runs near sensors and actuators to respond with low latency and maintain operation when connectivity is not available.

What it means to run AI on the edge

In robotics, on-board AI processes information within the equipment itself or in associated local computation. The robot does not need to send each piece of data to a remote service before interpreting a scene or deciding on the next action.

The cloud can continue to provide software distribution, analytics, or controlled skill sharing, but it is no longer a requirement for every operational decision.

Advantages for systems acting in the physical world

Each architecture must be sized according to the hardware, models and level of autonomy required.

Less Latency

It reduces the path between perception, decision and physical response.

Continuity

Allows local functions to be maintained when the network is limited or intermittent.

Data control

It makes it easy for sensitive information to be processed within the operating environment.

Physical integration

Bring the models closer to the robot's sensors, memory, skills, and controllers.

Frequently asked questions

Does onboard AI mean the cloud is never used?
No. It means that critical decisions can be executed locally. The cloud can be used for non-immediate tasks, distribution or analytics if the project requires it.
Do all AI models fit in one robot?
No. The design must balance compute capacity, memory, consumption, temperature and latency. That's why models are selected and optimized according to the platform.
What data should be processed locally?
It depends on the case, but immediate perception, control and sensitive data often benefit from local processing for latency, continuity or privacy.

Design a suitable architecture for your robot

We review hardware, sensors, models and response requirements.
Contact Lantanis