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In the context of UAS Traffic Management operations, where varied vehicle types and uncertain winds are expected, there is a need for a trajectory simulation that trades a vehicle-centric approach with a systems level approach to model expected vehicle performance with an uncertain operational environment. This paper focuses on the first phase of this trajectory prediction development. The goal is to understand the behavior of this new framework as applied to a vehicle targeting a specific terminal altitude. Specifically, a generalized six degree of freedom trajectory model was built to identify vehicle performance in the presence of wind. Generalization of this model was achieved by reducing the number of simplifying assumptions and using vehicle performance parameters, that were non-specific to the control system, to drive the control solution. The inner loop control strategy behind this algorithm is to find the forces and torques required to guide the vehicle to an intended altitude within vehicle-specific force and attitude constraints in the presence of winds. The control solution was optimized via the Artificial Bee Colony genetic optimization method. It was successfully demonstrated that this framework can utilize vehicle performance parameters, that were non-specific to the control system, to optimize a control solution for targeting an operational altitude. Additionally, vertical and lateral path deviations from the nominal case were observed in the wind case results. This forms the basis for quantifying the spatial and temporal requirements a vehicle will need for a given operation.

Managing trajectory separation of unmanned aircraft is critical to ensuring accessibility, efficiency, and safety in low altitude airspace. The concept of a geo-fence has emerged as a way to manage trajectory separation. A geo-fence consists of distance buffers that enclose individual trajectories to identify a 'keep-in' region and/or enclose areas that identify 'keep-out' regions. The 'keep-in' geo-fence size can be defined as a static number or calculated as a function of vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Given that the fleet of Unmanned Aircraft Systems (UAS) operating in low altitude airspace will be numerous and non-homogeneous, calculating a 'keep-in' geo-fence will need to balance operational safety and efficiency. A recently tested UAS Traffic Management (UTM) prototype used a geo-fence size of 30 meters, horizontally and vertically, for every operation submitted. The goal of this work is to determine the feasibility of a generalized, simple algorithm that calculates geo-fence sizes as a function of vehicle performance and potential wind disturbances. The resulting geo-fence size could be smaller or larger because the vehicle performance in the presence of wind is considered, thus leading to trajectory separation that is safe and efficient.In this paper, two simplified methods were developed to determine the feasibility of calculating a geo-fence as a function of vehicle parameters and wind information. The first method calculates the geo-fence using basic vehicle parameters and wind sensor data in a set of algebraic-geometric equations. The second method models a generic PID control system that uses a simplified set of equations of motion for the plant and uses gain scheduling to account for wind disturbances. It was found that the Algebraic-Geometric Geo-fence Algorithm provides geofence sizes of approximately 15 meters horizontally and 5 meters vertically, which is much smaller than the UTM static value of 30 meters. In the PID Controller Geo-fence Algorithm it was found that the geo-fence size is further reduced to less than 5 meters, horizontally and vertically. These results reveal that implementing geo-fence calculations provide UTM with the ability to schedule and separate operations based on geofences that are dynamic to vehicle capability and environment, which is more efficient than using a single static geo-fence.

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