Is this project an undergraduate, graduate, or faculty project?

Undergraduate

Project Type

group

Campus

Daytona Beach

Authors' Class Standing

Jack Capuano, Senior Austen Pallen, Senior Grace Gratton, Freshmen

Lead Presenter's Name

Jack Capuano

Lead Presenter's College

DB College of Engineering

Faculty Mentor Name

Dr. Watson

Abstract

The use of swarms such as unmanned aerial vehicles to solve problems is becoming more prevalent around the world. One promising application is drones in Search & Rescue operations. The modeling and simulation of these scenarios could improve the success rate and efficiency of those operations and in the case of Search and Rescue, help save lives. This research focuses on a previously created testbed that serves as a model of swarm operations under variable conditions, designed for a use case of drone Search & Rescue. The testbed can be used to analyze efficiency and success rate of various patterns and algorithms for drone swarms, such as consensus algorithms. Using this testbed, turtle hatching based consensus algorithms will be implemented into the drone swarm searching patterns and run through simulations with inclusion and exclusion of faulted agents and false positives/negatives. These results with then be compared to previous (non-consensus, centralized control) algorithms’ results.

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

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Implementation of Turtle-Based Consensus Algorithms on Drone Swarm Search & Rescue

The use of swarms such as unmanned aerial vehicles to solve problems is becoming more prevalent around the world. One promising application is drones in Search & Rescue operations. The modeling and simulation of these scenarios could improve the success rate and efficiency of those operations and in the case of Search and Rescue, help save lives. This research focuses on a previously created testbed that serves as a model of swarm operations under variable conditions, designed for a use case of drone Search & Rescue. The testbed can be used to analyze efficiency and success rate of various patterns and algorithms for drone swarms, such as consensus algorithms. Using this testbed, turtle hatching based consensus algorithms will be implemented into the drone swarm searching patterns and run through simulations with inclusion and exclusion of faulted agents and false positives/negatives. These results with then be compared to previous (non-consensus, centralized control) algorithms’ results.

 

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