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