Skip to content
tvdtogt edited this page May 6, 2020 · 17 revisions

image of explorer board with sensors

The Astroplant Explorer Kit is an educational tool to help students understand the Astroplant project and design their own experiments. Instead of working on one (relatively expensive) installation for the whole class, students can have their own set-up to try things out. The kit can be used in different ways: For building up everything step by step, or as a working basis for further development.

Requirements

The Explorer Kit should be fully compatible with the set-up of the official kit: meaning the software of the Astroplant repository should work on the explorer kit without changes. This also means that the sensors and configuration of the Raspberry Pi should be the same. The whole kit should be affordable, and parts should be easy to get. (total price < EUR 100) The explorer kit should be extensible: when students want to try out new things, they should not be restricted by the design.

Conceptual

Inspired by the Snips developer kit, we made a perforated board on which a Raspberry Pi, sensors and actuators can be fixed. In addition, we designed a very simply break-out hat (PCB) for a Raspberry Pi with Grove connectors. Many components are widely available with Grove connectors. Grove connectors will make assembly easy and minimize the risk of mistakes. But alternatively jumper wires can be used when the Grove connectors are not enough.

Project

Teachers can decide on their own project approach, depending on the age, knowledge and interest of students involved. Several building blocks will be made available with documentation, code and (if necessary) additional hardware. Since the Astroplant is using a Raspberry Pi, some knowledge of Linux (Raspbian) and Python is recommended.

How to contribute

If you decided to buy a kit, you will be programming it in Python 3. And you may be creating new things. Perhaps you are optimizing code. Or you attach new, better or cheaper sensors. Or optimize plant growth control algorithms. The community will benefit from your work if you share this on our GitHub.