Tutorial: Using NVIDIA Jetson as an AI Brain for Robotics with ROS 2

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A new developer tutorial outlines how to build a robot system using NVIDIA Jetson for high-level AI perception and decision-making, while delegating low-level motor control to a separate microcontroller. The architecture relies on ROS 2 as the middleware framework, with nodes handling distinct tasks such as sensor input, object detection, navigation, and safety logic. The guide walks developers through setting up a ROS 2 workspace on Jetson, creating packages in Python or C++, and verifying the environment with a basic publisher-subscriber pipeline. It also covers how a Flutter-based operator interface can connect to the robot through a secure gateway rather than exposing the ROS graph directly to the internet. The tutorial emphasizes version compatibility between JetPack, CUDA, TensorRT, Isaac ROS, and ROS 2 as a critical consideration before installation.

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