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.

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Fixed Wing Unmanned Aerial Vehicles (UAVs) performing Intelligence, Surveillance and Reconnaissance (ISR) typically fly over Areas of Interest (AOIs) to collect sensor data of the ground from the air. If needed, the traditional method of extending sensor collection time is to loiter or turn circularly around the center of an AOI. Current Autopilot systems on small UAVs can be limited in their feature set and typically follow a waypoint chain system that allows for loitering, but requires that the center of the AOI to be traversed which may produce unwanted turns outside of the AOI before entering the loiter. An investigation was performed to compare the current loitering techniques against two novel smart loitering methods. The first method investigated, Tangential Loitering Path Planner (TLPP), utilized paths tangential to the AOIs to enter and exit efficiently, eliminating unnecessary turns outside of the AOI. The second method, Least Distance Loitering Path Planner (LDLPP), utilized four unique flight maneuvers that reduce transit distances while eliminating unnecessary turns outside of the AOI present in the TLPP method. Simulation results concluded that the Smart Loitering Methods provide better AOI coverage during six mission scenarios. It was also determined that the LDLPP method spends less time in transit between AOIs. The reduction in required transit time could be used for surveying additional AOIs.