Autonomous model selection for surface classification via unmanned aerial vehicle

<p>In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.</p>

Name thesis:degree_name

Master of Science in Engineering (M.S.Eng.)

Level thesis:degree_level

Thesis - Pacific Access Restricted

Discipline thesis:degree_discipline

Engineering

Year dc:date.available

2017

Author dc:creator

• Watts-Willis, Tristan A.

Contributors dc:contributor

• Elizabeth Basha

• Robotics
• Artificial intelligence
• Applied sciences
• Automated classifier
• Classification
• Efficiency
• Feature selection
• Model selection
• Unmanned aerial vehicle
• Engineering

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Identifier

9781369757897

OAI identifier oai:identifier

oai:scholarlycommons.pacific.edu:uop_etds-1223