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.
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