Mission Planning For Unmanned Aerial Vehicles

The purpose of this thesis is to produce a closed-loop feedback mission planning tool that allows for the operator to control multiple Unmanned Aerial Vehicles (UAV) within a mission. Different styles of UAVs and mission planners that are available on the market were evaluated and selected for their cost, size, ability to customize, and fit for mission work. It was determined that commercially available mission planners do not provide the level of automation required, such as allowing for different algorithms for assigning UAV tasks and for planning UAV flight paths within a mission. Comparisons were made between different algorithms for path planning and tasking. From these comparisons, a bio-inspired machine-learning algorithm, Genetic Algorithm (GA), was chosen for assigning tasks to UAVs and Dubins path was chosen for modeling UAV flight paths within the mission simulation. Since market mission planners didn't allow for control of multiple UAVs, or wouldn't allow for the operator to add algorithms to increase usability and automation of the program, it was decided to create a Graphic User Interface (GUI) that would allow the operator to customize UAVs and the mission scenario. A test mission scenario was then designed, which included 9 Points of Interest (POI), 1 to 3 Targets of Interest (TOI), 3 to 5 UAVs, as well as simulation options that modeled failure of a task or a UAV crash. Operator feedback was incorporated into the simulation by allowing the operator to determine a course of action if a failure occurred, such as reprogramming the other UAVs to complete the tasks left by the crashed UAV or reassessing a failed task. Overall mission times decreased for reprogramming the UAVs versus running a separate mission to complete any tasks left by the crashed UAV. Additional code was added to the GA and Dubins path to increase speed without decreasing solution fitness.