Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind

D'Souza, Sarah; Ishihara, Abe; Nikaido, Ben; Hasseeb, Hashmatullah · 2016 · OpenAlex-citations

DOI: 10.1109/dasc.2016.7777987

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Summary

This paper addresses the challenge of managing trajectory separation for Unmanned Aircraft Systems (UAS) in low-altitude airspace, specifically focusing on the feasibility of dynamic geo-fencing. Current UAS Traffic Management (UTM) prototypes often employ static geo-fences, such as a uniform 30-meter buffer, which can be inefficient given the heterogeneous nature of UAS fleets. The authors propose a generalized algorithm to calculate geo-fence sizes based on specific vehicle performance characteristics and environmental wind disturbances, aiming to balance operational safety with airspace efficiency. To evaluate this concept, the researchers developed two simplified methods. The first, the Algebraic-Geometric Geo-fence Algorithm (AGGA), utilizes basic vehicle parameters—maximum airspeed and control response time—along with wind sensor data in algebraic-geometric equations. This method treats the vehicle as a point mass and calculates displacement due to wind over a fixed time step. The second method, the PID Controller Geo-fence Algorithm (PCGA), employs a simplified rigid-body dynamics model with a Proportional-Integral-Derivative (PID) controller. The PCGA uses gain scheduling, optimized via an Artificial Bee Colony genetic algorithm, to adapt to varying wind conditions, providing a more granular simulation of trajectory deviations. The study integrated three distinct wind models: NOAA High Resolution Rapid Refresh (HRRR) forecast data, real-time sensor data from the California State University-Mobile Atmospheric Profiling System (CSU-MAPS), and Computational Fluid Dynamics (CFD) simulations using OpenFOAM to model urban wind flows around buildings. The AGGA was tested against HRRR and CSU-MAPS data for ten different multirotor vehicles, while the PCGA utilized the OpenFOAM-generated wind fields. The results demonstrated that dynamic geo-fencing significantly reduces buffer sizes compared to static standards. The AGGA produced horizontal geo-fences of approximately 15 meters and vertical fences of 5 meters, roughly half the size of the static 30-meter UTM standard. The PCGA further reduced these dimensions to less than 5 meters in both horizontal and vertical axes. The study concludes that implementing geo-fence calculations based on vehicle capability and environmental data allows for more efficient airspace scheduling and separation than static buffers, particularly as wind conditions and vehicle performance vary.

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