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I am doing a project for a machine learning course where I perform GNC with a turtlebot3, but utilize ML algorithms instead of the classic non-ML algorithms.

The objective is to reach an orange cone through an obstacle environment. The ML algorithms are:

Camera - objective detection - detect the cone's location - supervised learning - YOLO algorithm

LiDAR and Encoders - SLAM - detect obstacles and localize - unsupervised learning - unsure of ML algorithm

Encoders - Control - determine velocity command to travel along path - Reinforcement learning - PPO

So the gist of the plan is that using ML algorithms, the camera will detect the objective, the LiDAR and encoders will perform SLAM and the encoders and an A* path will determine the control commands.

I've got the turtlebot working so I next need to understand the ROS layout. I'm thinking my ROS graph will look something like the following. Does this look right? I am new-ish to ROS and so am trying to understand how this all works.

enter image description here

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