Incident Response Enhancements using Streamlined UAV Mission Planning, Imaging, and Object Detection

Systems composed of simple, reliable tools are needed to facilitate adoption of Uncrewed Aerial Vehicles (UAVs) into incident response teams. Existing systems require operators to have highly skilled level of knowledge of UAV operations, including mission planning, low-level system operation, and data analysis. In this paper, a system is introduced to reduce required operator knowledge level via streamlined mission planning, in-flight object detection, and data presentation. For mission planning, two software programs are introduced that utilize geographic data to: (1) update existing missions to a constant above ground level altitude; and (2) auto-generate missions along waterways. To test system performance, a UAV platform based on the Tarot 960 was equipped with an Nvidia Jetson TX2 computing device and a FLIR GigE camera. For demonstration of on-board object detection, the You Only Look Once v8 model was trained on mock propane tanks. A Robot Operating System package was developed to manage communication between the flight controller, camera, and object detection model. Finally, software was developed to present collected data in easy to understand interactive maps containing both detected object locations and surveyed area imagery. Several flight demonstrations were conducted to validate both the performance and usability of the system. The mission planning programs accurately adjust altitude and generate missions along waterways. While in flight, the system demonstrated the capability to take images, perform object detection, and return estimated object locations with an average accuracy of 3.5 meters. The calculated object location data was successfully formatted into interactive maps, providing incident responders with a simple visualization of target locations and surrounding environment. Overall, the system presented meets the specified objectives by reducing the required operator skill level for successful deployment of UAVs into incident response scenarios.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Link, Eric Matthew

Chair dc:contributor.committeechair

• Kochersberger, Kevin Bruce

Committee members dc:contributor.committeemember

• Leonessa, Alexander
• Komendera, Erik

• UAV
• Incident Response
• Object Detection

Statement dc:rights

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37833

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115602