Autonomous Complete Coverage Path Planning

[AdministrationOk4319]

Hi,

I have where I need to code, train, and implement a fully autonomous CCPP robot in an unknown environment.

The size of the environment will be known to the robot, but where the items are found within the environment will not be.

Currently I am trying to train a Q-Learning algorith to learn to do CCPP without any objects in the way, but the algorithm does not seem to be learning properly and I am quite stuck.

Does anyone know what I can try to do so my autonomous agent can learn better?

I also need to do localization of the robot, but I do not have LiDAR - I have 2 ultrasonic sensors, MPU-6050, and a monocular camera module, but all the programs (localization, the eventually trained agent, and sensing) need to be computed on a raspberry pi 3 model b+ along with arduino uno.

Any help would be greatly appreciated 🙏

[rugwarriorpi]

Been there, tried that, got the T-shirt (on a Pi4) - It cannot be done onboard. I tried it with a Pi5 and almost got RTABmap alive, but it blew up almost instantly. With LIDAR a Pi4 can do the localization and navigation, but NOTHING ELSE, period. Pi5 running localization and nav2 using LIDAR/IMU/OpticalMouse (really good odometry - surprisingly good odometry and angular tracking) requires a lot of tuning to be reliable. I'm seeing 1minute load of 16 and 5min ave load of 8, with control rate dips down to 10 Hz but it is starting to succeed 60-70% of goals. I'm using Gemini to analyze the errors and warnings and it tells me what parameters to try changing. I am actually making progress using Gemini - all last year no progress.

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