Open Source UAV and RadioFruits

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This post contains my technical notes, links and hardware tests related to the integration and monitoring of several sensors through a LoRa link with Adafruit technology.

This "backpack" solution can be used to monitor at a very low cost, and non-invasively, some flight variables in radio control models and drones.

Hardware

The hardware selected to run the tests was based on "RadioFruits", the line of boards Adafruit Feather. Specifically, the product Adafruit Feather M0 RFM95 LoRa Radio. With two units of this product we can obtain sending/receiving radio units.

The transmitter board can be completed with a GPS sensor supported through the Adafruit GPS featherwing extension, a temperature/humidity/pressure sensor (Adafruit BMP280) and a 9-DoF kit (MPU-9250)

Some images of the prototype:

Software

On the soft side I worked with Sloeber (v4.3.1), a free and open source Eclipse IDE that can be used as an alternative to the Arduino IDE.

The Sketches

A sketch is the name that the Arduino community uses for a program. It's the unit of code that is uploaded to and run on the boards.

These were the sketches used for the tests:

Test session and logging

The following log shows information related to identification, flashing, initialization and communication test between two units running the previous sketches:

Technical documentation and datasheets

Wrapping up

After adding the sensors and programming the boards, the communication tests were successful. The maturity of the hardware and software stack is good. The stack is also well documented.

As for the variables of interest monitored in the tests, these were the attitude, height, speed and GPS coordinates.

Given the results obtained, the solution could be useful as a support tool to study and understand maneuvers performed by radio control models and drones on short-range flights where the drift accumulated in the sensors is not a problem and the absence of great precision in the measures is not significant to obtain information and conclusions of value.

More demanding scenarios and use cases would require replacing the sensors with others of greater precision and performance.

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