We developed and tested a flexible genetic algorithm-based system for tasking a team of unmanned aerial vehicles to complete a coordinated surveillance mission. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.
Paper: A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing published in the Journal of Unmanned Systems in January 2015.
Control of multiple unmanned aerial vehicles is of importance given that so many have been deployed in the field. This work focused on how genetic algorithms have been applied to the cooperative tasking of the AeroVironment’s Raven unmanned aerial vehicle engaged in an intelligence, reconnaissance, and surveillance mission.
Paper: Using Genetic Algorithms for Tasking Teams of Raven UAVs published in the Journal of Intelligent & Robotic Systems in July 2012.