Localize your cat at home with BLE beacons, ESP32s, and Machine Learning #BLE #Bluetooth #ML #ESP32 @Raspberry_Pi

Have you ever wanted to be able to know where your cat is in the house? This project can be used for localization the position of a cat (or anything else, re. kids) in a building using Bluetooth low energy (BLE) beacons attached to the object, a set of cheap ESP32 detectors, and Machine Learning models.

This is an overview of a pipeline for creating an inhouse cat locator. Actually, it can be applied to any animal (including humans) or object, and any building. The system works as follows:

• The cat with a small BLE beacon, is emitting BLE signals
• BLE signal is detected by ESP32s located here and there; they are measuring he signal strength of the BLE beacon.
• Each ESP32 sends data to the server (database)
• The python program is fetching the last measurements from all ESP32 detectors (i.e., signal strength values)
• Using trained machine learning models it predicts the location of the cat

The challenge here is to use a number of detectors which is significantly lower than the number of rooms and make ML do the rest – 4-6 for moderate house/flat should be enough. One can also use a modern Raspberry Pi with BLE.

See the project details with source code on GitHub.