Automation is a lofty goal in many industries, but not always straightforward to execute. Welding car bodies in the controlled environment of a production line is relatively straightforward. Maintaining plants in a greenhouse, however, brings certain complexities due to the unpredictable organic processes at play. Hexagrow is a robot that aims to study automation in this area, developed as the final year project of [Mithira Udugama] and team.
The robot’s chassis is a very modern build, consisting of carbon fiber panels and 3D printed components. This kind of strength is perhaps overkill for the application, but it makes for a very light and rigid robot when the materials are used correctly.
It’s the sensor package where this build really shines, however. There’s the usual accoutrement of temperature and humidity sensors, and a soil moisture probe, as we’d expect. But there’s more, including an impressive soil pH tester. This involves a robotic arm with a scoop to collect soil samples, which are then weighed by a load cell. This is then used to determine the correct amount of water to add to the sample. The mixture is then agitated, before being tested by the probe to determine the pH level. It recalls memories of the science packages on Mars rovers, and it’s great to see this level of sophistication in a university project build. There’s even a LIDAR mounted on top for navigation purposes, though it’s not clear as to whether this sensor is actually functionally used at this point in development.
The main idea was to create a drone that could autonomously follow a target which provided GPS data for the drone to follow. [Ryan] planned to implement this by having a smartphone provide GPS coordinates to the drone over WiFi, allowing the drone to track the user.
As this was a university project, he had to take a very carefully considered approach to the build. Given likely constraints on both money and time, he identified that the crux of the project was to develop the autonomous part of the drone, not the drone itself. Thus, off-the-shelf parts were selected to swiftly put together a drone platform that would serve as a test bed for his autonomous brain.
The write up is in-depth and shares all the gritty details of getting the various subsystems of the drone talking together. He also shares issues that were faced with altitude control – without any sensors to determine altitude, it wasn’t possible to keep the drone at a level height. This unfortunately complicated things and meant that he didn’t get to complete the drone’s following algorithm. Such roadblocks are highly common in time-limited university projects, though their educational value cannot be overstated. Overall, while the project may not have met its final goals, it was obviously an excellent learning experience, and one which has taught him plenty about working with drones and the related electronics.