Date of Award

August 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical Engineering

First Advisor

Jeremiah Neubert

Abstract

Unmanned Aerial Systems (UAS) in Search and Rescue (SAR) operations,combined with teaming configurations and optimized path planning algorithms, enhance efficiency in locating and assisting disaster victims. Flooding accounts for 90% of natural disasters in the USA, necessitating rapid scouting and supply drops in affected areas. Current UAS path planning lacks proper start and end point selection and does not use multiple aircraft collaboratively.

This study introduces a UAS teaming strategy with high-altitude leader drones for area coverage and lower-altitude follower drones for detailed data collection and supply drops. The focus is optimizing follower drone path plan- ning using the Open-Loop Traveling Salesperson Problem (OTSP) framework while testing various UAS teaming methods.

The proposed UAS teaming and path-planning strategies were validated through simulations measuring path length, total completion time, and target supply wait times across different SAR scenarios. UAS teaming configurations were tested and validated, considering leader speed, follower speed, supply drop times, and the number of followers as key parameters. Path planning algorithm efficiency was compared against optimal solutions produced by a Breadth First Search (BFS) algorithm.

The results confirmed that the optimized algorithms significantly enhance UAS operational efficiency in SAR missions. Follower speed affected total completion time by only 1%. Utilizing more than six follower aircraft did not significantly improve completion time or decrease target wait time, only affecting each by 1-2 minutes. Furthest Insertion was the best path planning algorithm tested, generating optimal solutions on ten-point datasets over half the time and remaining computationally quick, generating paths in under ten milliseconds.

Download

Share

COinS