# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 8 Conclusions and Future Work  
This paper applied airspace geofencing volumization and path planning to support UTM management of low-altitude airspace. Layered durational geofences wrapping flight trajectories ensure the UAS will fly without conflict in designated or reserved airspace volumes. Our airspace volumization algorithms generated four conflict-free paths for any keep-in/keep-out geofence volume set based on turn, constant cruise, terrain follower and combination turn/cruise options. The algorithm ranked these paths using a weighted distance, energy, and time cost function, then selected the minimum-cost solution. A city map data of Lower Manhattan was used to construct keep-out geofences around buildings. Monte Carlo simulation studies validate our geofence algorithms and support the statistical characterization of performance including run time. A benchmark comparison of our dynamically geofenced flight plans and conventional flight corridor solutions is provided, showing that our solutions reduce flight distance and power compared to fixed corridor solutions. A case study of two sUAS flight planning demonstrated how the proposed geofencing pipeline supports multiple sUAS deconfiction. Algorithms and definitions from this paper can contribute to future UTM dynamic airspace geofencing operational standards. This work simplifies flight planning to geometric paths. Future work will incorporate aircraft dynamics into flight plans and geofence layer sizing, extend airspace volumization  
Figure 23: Example of a 3-D geofence wrapping a “terrain follower” flight plan for \(sUAS_{2}\). The \(sUAS_{2}\) trajectory is shown in black, and the \(sUAS_{1}\) trajectory is shown in blue. Polyhedra (green) denote keep-out geofences around buildings. The remaining 2-D polygons denote keep-out geofences around buildings that are outside the combined ROI. Figure 22: Example of a 3-D geofence wrapping a “constant cruise altitude” flight plan for \(sUAS_{2}\). The \(sUAS_{2}\) trajectory is shown in black, and the \(sUAS_{1}\) trajectory is shown in blue. Polyhedra (green) denote keep-out geofences around buildings. The remaining 2-D polygons denote keep-out geofences around buildings that are outside the combined ROI. to enclose cooperative groups of sUAS. Additionally, the altitude constraint and other factors such as day/night local population density, GPS dependency, air traffic volume, and vehicle-specific parameters should be incorporated in the geofenced path planning algorithm to generate solutions that are more realistic for UTM-specific applications. We hope to apply machine learning to large-scale flight track data and urban maps to generalize and optimize geofencing volume designs based on area topology, day/night occupancy, infrastructure, and existing air traffic patterns. We will also explore auto-code generation and Python/C++ implementations to improve path planning computational performance. Conceptualization, J.K. and E.A. ; methodology, J.K. and E.A. ; software, J.K.; validation, J.K. and E.A. ; writing--original draft preparation, J.K.; writing--review and editing, J.K. and E.A. ; visualization, J.K.; supervision, E.A. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Collins Aerospace (Funder grant number: AWD014974) and by NASA (Funder grant number: T18-601046- UMICH). Not applicable. The authors gratefully acknowledge the following people who supported this research: [PERSON] for geofencing simulation software and support. [PERSON] for the conceptualization of operational and inverse volumization, [PERSON] for the conceptualization of the trajectory geofence and flight power consumption data, and [PERSON] for pre-processed Manhattan map data and documentation. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: