RobotThymio: AI Deep Learning (Q-Learning Algorithm)

• piCamera + Lidiar Sensor + Thymio's Sensors
• Raspberry Pi 4

Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. IT is also a type of reinforcement learning.

With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn. Good actions are rewarded or reinforced, while bad actions are discouraged and penalized.

With the state-action-reward-state-action form of reinforcement learning, the training regimen follows a model to take the right actions. Q-learning provides a model-free approach to reinforcement learning. There is no model of the environment to guide the reinforcement learning process. The agent -- which is the AI component that acts in the environment -- iteratively learns and makes predictions about the environment on its own.

Connect to Rasberry Pi Server

• username: pi

• ssh pi@Robo.local

• Password : RoboRobo

Inside Rasberry Pi (terminal 1)

• flatpak run --command=thymio-device-manager org.mobsya.ThymioSuite

Inside Rasberry Pi (terminal 2)

• cd ~/BreezySLAM/python/breezyslam

• python3.11 SLAM.py

visualizer (terminal 3 )

• cd visualizer
• python3.11 Visualizer.py

Adding files directly to the raspberry pi (simulators, controllers)

• run the server pi@Robo:
• open another terminal locally and run : scp <path_of_the_file> pi@192.168.0.11:/home/pi/
• add password : RoboRobo

Visualization gnu plot

• run gnuplot

• load "simple_visualization.gnu"

Lidiar Tests

cd distanceTestsLidiar

Image Processing

• cd image_processing : Image recognition, object detection, colour recognition.

Simulation-Deep-Learning

cd simulation-deepLearning > q-learning-simulation.py

Controller

cd Controller > Robot_Controller.py