Sub-projects for my Master's Thesis: developing a 'Digital Twin' model of a harvest aid robot and translating field data into simulated grapevine rows.
Main focus of my Master's Thesis: developing two path-planning algorithms for a non-holonomic mobile robot and comparing their efficacy in an agricultural setting.
Aerospace research project studying orbital mechanics, optimal flight path trajectories, green rocket fuels and novel navigational paradigms.
Simulated study of a orchard nursery robot designed to traverse a field and estimate the sizes of trees.
Several machine vision projects completed for ECS 174 - Computer Vision
Fashion waste research project designing and assembling a machine to reuse and up-cycle fabric scraps.
Agricultural engineering research project designing installing and testing a motor controller to automate an apple picking platform.
Course project designing, fabricating and competing with a high efficiency water pump.
Course project exploring entrepreneurship and rapid prototyping in mechanical engineering.
Course project assembling and operating a scanning laser microscope, with emphasis on engineering communication and reporting.
An overhead view as the craft is maneuvered into lunar orbit.
This individual project was completed over the duration of EBS 289K: Sensors and Actuators in Agriculture. The goal of this project was to develop, in simulation, a robot which could efficiently traverse a tree nursery and use a LIDAR data to estimate the trunk sizes.
I began the project by implementing a genetic algorithm to solve the “traveling salesman problem” and find a sequence through a field of rows. Then, I developed an algorithm to create a Dubin's path through that sequence.
In simulation, I used an Extended Kalman Filter to take noisy GPS and odometery data and fuse these sources of information to perform accurate localization of the robot. I developed a simple pure pursuit algorithm to follow the generated path using the localization data.
Using noisy, simulated LIDAR data taken along the path, I created a probability grid representing the occupied locations on a digitized map of the tree nursery.
Finally, using this probability grid and built-in MATLAB computer vision tools, the program outputted a location and diameter of each detected tree in the nursery.
I analyzed this outputted data against the known solution and found that work must be continued to increase the accuracy of the system, but the project was overall very successful. A full report produced by this project can be found in the writing samples page.
The pump performing on test day.
Video commercial for the Swishkey.
Copyright © 2019 Rayne Milner- All Rights Reserved.
Ad astra per aspera.