Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Jack Capuano, Junior
Lead Presenter's Name
Jack Capuano
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Watson
Abstract
The use of unmanned aerial vehicle swarms to solve problems is becoming more prevalent around the world. One promising application is for Search & Rescue operations. The modeling and simulation of these scenarios could help improve the success rate and efficiency of those operations and therefore save lives. This research presents a testbed simulation that will model a fleet of drones searching for a target or targets using different search patterns under variable conditions. Using this simulation to study the effects of certain variables like drone path patterns, scenario environmental conditions, and others will lead to a further understanding of what impacts they have on the outcome of the search. The simulation tests have one goal; to decrease the time spent before finding the target. Preliminary results from the alpha simulation show that the saturation point wherein adding more drones stops reducing the time to find the target is around 3.2 drones per square mile. Adding more drones past this point has diminishing returns, as 3.2drones/mi^2 is 90% efficient. Future work includes adding and improving to the functionalities of the simulation, as well as implementation of consensus algorithms
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Development and Verification of a new Drone Swarm Search and Rescue Model
The use of unmanned aerial vehicle swarms to solve problems is becoming more prevalent around the world. One promising application is for Search & Rescue operations. The modeling and simulation of these scenarios could help improve the success rate and efficiency of those operations and therefore save lives. This research presents a testbed simulation that will model a fleet of drones searching for a target or targets using different search patterns under variable conditions. Using this simulation to study the effects of certain variables like drone path patterns, scenario environmental conditions, and others will lead to a further understanding of what impacts they have on the outcome of the search. The simulation tests have one goal; to decrease the time spent before finding the target. Preliminary results from the alpha simulation show that the saturation point wherein adding more drones stops reducing the time to find the target is around 3.2 drones per square mile. Adding more drones past this point has diminishing returns, as 3.2drones/mi^2 is 90% efficient. Future work includes adding and improving to the functionalities of the simulation, as well as implementation of consensus algorithms