The main objective of an Unmanned-Aerial-Vehicle (UAV) is to provide an operator with services from its payload. Currently, to get these UAV services, one extra human operator is required to navigate the UAV. Many techniques have been investigated to increase UAV navigation autonomy at the Path Planning level. The most challenging aspect of this task is the re-planning requirement, which comes from the fact that UAVs are called upon to fly in unknown environments. One technique that out performs the others in path planning is the Genetic Algorithm (GA) method because of its capacity to explore the solution space while preserving the best solutions already found. However, because the GA tends to be slow due to its iterative process that involves many candidate solutions, the approach has not been actively pursued for real time systems. This paper presents the research that we have done to improve the GA computation time in order to obtain a path planning generator that can recompile a path in real-time, as unforeseen events are met by the UAV. The paper details how we achieved parallelism with a Field Programmable Gate Array (FPGA) implementation of the GA. Our FPGA implementation not

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Surveillance applications of unmanned aerial vehicles within urban areas is made difficult by turbulent winds generated by buildings. A methodology is proposed by which urban wind data is evaluated, selected, and applied during flight simulation. The urban environment is represented by a combination of discrete single buildings and canyons each easily amenable to computational fluid dynamics. These simulations are used to generate a database of building wakes for various wind conditions and building configurations typical to the North American urban environment. A selection algorithm is used that determines if the current aircraft position is influenced by the building wake and, if so, calculates the resulting effect on the aircraft. Results are presented for a Yamaha R-50 R/C helicopter in both forward and vertical flight. Variations in aircraft attitude by as much as 10 are observed when considering building wake effects generated by a 8 kt wind. It is also demonstrated that this approach is able to identify locations within a given wake at which aircraft flight is most affected. Nomenclature

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