TP – Intruder Detection Prerequisites / knowledge: Principle of ultrasound Knowledge of 2-layer neural networks (ie without hidden layers)
Goal: You may have noticed that in some learning, especially those using the camera, layers of intermediate neurons are added. We will show you a simple example that allows you to better understand the difference that intermediate layers of neurons can bring. Here we will use the robot's ultrasound to make it act as an intruder detector; the robot will have to turn its wheels when it sees an intruder in front of it.
Startup To start, turn on the robot and position it a few tens of centimeters from a wall, so that it cannot move when its wheels are turning. Take a flat object such as a box for example that you can place between the wall and the robot to symbolize the intruder.
Configuration : Cf. scenario "detection of intruders" part: "preparation phase of the work environment" Learning phase without intermediate layer Cf. scenario "detection of intruders" part: "Learning phase without intermediate layer" As it is a supervised learning, it is first up to you to show the robot how to behave. First of all do not put anything between the robot and the wall and press several times on the stop action next to the neural network. Then place the obstacle between the robot and the wall, in a very perpendicular way, and press "beep" several times Question: When you remove the obstacle, the robot continues to beep. Why does the robot do this?
Answer The only input to the neural network is the distance calculated byultrasound. From this distance, the network will calculate a prediction for each of the two possible actions, namely beep or stand