Ahlers, Corey (2022) Development Of an Adaptive Control Mechanism to Enable Automated Irrigation on Gantry Robots. [USQ Project]
|
Text (Project)
Ahlers_C_Humpal_Redacted.pdf Download (2MB) | Preview |
Abstract
Currently there is a need for automated Space Agriculture systems to detect signs of early plant stress, to ensure both food safety and security on board space missions. Astronauts are hindered by the need to maintain constant communication to accurately diagnose plant stresses and maintain nutritional and psychologically beneficial plant life; so, a need for launch ready automation software has become apparent. Without a system capable of detecting early plant stresses to ensure food safety and security, further resources are required to address these problems manually. This increase of resources includes the time needed to facilitate communication with Earth based specialists to diagnose specimens, resulting in a loss of valuable time in preventing plant stress.
From the examined literature, the area of automated Space Agriculture has had little practical application with most automated agricultural projects such as irrigation and monitoring, performed on Earth through gantry topologies. Gantry topologies refer to a structure consisting of an overhead bridge beam supported by a platform, which allows a manipulator to move along multiple axis. Therefore, there is a knowledge gap in formulating a system that is capable monitoring and adapting to the condition of plant specimens through an automated adaptive control algorithm to maintain existing plants in microgravity environments.
Through this knowledge gap, the project aimed to answer if greenhouse gantry topologies are capable of reliably interpreting and adapting automatically in real time, facilitating plant life in isolated conditions. This question was answered through the development of an adaptive control plugin that was used in conjunction with a FarmBot Genesis XL gantry topology. The FarmBot topology was able to capture a total of 30 images of individual plant specimens which were used by the adaptive control plugin. The adaptive control plugin was able to process these plant specimens and adapt upon the change in specimen area with recommendations of applied water to each specimen.
Results showed that although the adaptive plugin was able to process data, inconsistencies were found in the processing and storage of plant area, specimen recognition and generated adaptive water values. These inconsistences formed recommendations on the further operation and development of the FarmBot and adaptive control topologies.
|
Statistics for this ePrint Item |
| Item Type: | USQ Project |
|---|---|
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -) |
| Supervisors: | Humpal, Jacob; McCarthy, Cheryl |
| Qualification: | Bachelor of Engineering (Honours) (Electrical and Electronic) |
| Date Deposited: | 19 Jun 2023 04:01 |
| Last Modified: | 20 Jun 2023 01:10 |
| Uncontrolled Keywords: | Space agriculture; Machine Vision; Robotics; Automation; Adaptive Control |
| URI: | https://sear.unisq.edu.au/id/eprint/51874 |
Actions (login required)
 |
Archive Repository Staff Only |
