This paper discusses the development and testing of a flexible genetic algorithm (GA)-based system used for tasking a team of unmanned aerial vehicles (UAVs) to complete a coordinated surveillance mission. The GA development, laboratory testing of the GA to ensure convergence to a \good" solution, integration testing with two ground stations, and the field testing of the algorithms are explained. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.
The focus of this study was to examine an automated mission planner that utilized a heterogeneous set of small aerial assets simultaneously surveying several Points of Interest (POI). The concept mission was to aerially search for an unknown Target of Interest (TOI) located amongst a set of POIs. In order to develop a planning system that is capable of meeting mission requirements, an adaptive mission planner, based on a Genetic Algorithm (GA), was investigated that seeks to task a heterogeneous set of unmanned aerial vehicles. Initially, a set of fixed wing UAVs were tasked to survey a set of POIs in search of a TOI, a POI that requires additional or long term surveillance. Once a TOI was located, a multi-rotor UAV was deployed to visit the TOI and additional POIs. Remaining POIs that were not tasked to the multirotor UAV were then re-tasked amongst the fixed wing aircraft set. The mission planner was implemented using a GA that planned initial and post TOI identification UAV paths. Mission simulations were conducted and the mission time increase was analyzed against different TOI locations and number of UAVs searching. Simulation results indicated that the deployment of a multi-rotor UAV not only provided additional surveillance of the TOI, but reduced overall mission times as well.
A Heterogeneous Aerial Platform Mission Planner using a Genetic Algorithm by Jonathan Rojas Systems exist today that can plan a mission with more than one aircraft efficiently for surveillance. However, objectives in these missions do not change and are typically performed using a homogeneous set of aerial vehicles. An adaptive mission planner was sought to task a heterogeneous set of Unmanned Aerial Vehicles (UAVs) when an unknown Target of Interest (TOI) is located amongst a set of Points of Interest (POIs). First, two dimensional flight path models of fixed wing and quadcopter platforms were created. Next, the design of a genetic algorithm and its fitness functions were studied. Fixed wing fitness functions were developed to balance POI task loads amongst a set of fixed wing aircraft. A quadcopter fitness function was then designed to task a quadcopter to visit a newly located TOI. The quadcopter fitness function was also designed to maximize battery usage as it was desired that the quadcopter visit as many additional POIs on route to and from the TOI. Case studies were then simulated using varying heterogeneous UAV sets and TOI locations. Results of these simulations were then analyzed using mission times as a performance metric. Simulation results indicated that the deployment of the quadcopter to the TOI and additional POIs reduced overall mission times. Mission time reductions were also found to be depended on the number of fixed wing aircraft used in heterogeneous UAV sets. I would also like to thank my committee members Dr. Marvin Cheng, Dr. Marjorie Darrah and Dr. Mario Perhinschi for their suggestions and edits on this thesis. I would lastly like to thank my family for all the love and support they have given me during my time here at WVU. I would not be where I am today without their love and support.
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