Embedded Files
Urban airspace availability assessment (below 150 m)
Urban airspace availability assessment (below 150 m)
Summary
Background
Urban airspace is an important space where many value-creating aerial activities such as drone delivery may take place in the near future. However, not all vacant parts of the airspace are available because some areas may be blocked from use to minimize risks to people, infrastructure, and other air users.
Objective
This study aimed to find safe or available airspace that aerial vehicles can navigate within an acceptable level of risk, considering the operational capability of a vehicle and the level of protection for surrounding infrastructure.
Contribution
This study highlights that low-altitude urban airspace is fundamentally different from high-altitude airspace, in terms of geospatial complexity arising from obstacle configurations. Particular research efforts to examine such complex-shaped map environment are necessary for airspace management and flight planning of large-scale traffic in a city-wide 3D map.
Related publication
Cho, J., & Yoon, Y. (2018). How to assess the capacity of urban airspace: A topological approach using keep-in and keep-out geofence. Transportation Research Part C: Emerging Technologies, 92, 137-149. IF: 6.077 [pdf]
Cho, J., & Yoon, Y. (2018). Assessing the airspace availability for sUAV operations in urban environments: A topological approach using keep-in and keep-out geofence", International Conference on Research in Air Transportation 2018 Doctoral Symposium (ICRAT 2018). [pdf]
2D snapshot of airspace at 70 meters: the amount of available airspace reduces as the containment volume (i.e. safety margin) of aircraft increases.
red: airspace inaccessible by aircraft with containment limit of size r, increased from 10m to 30m;
black: airspace occupied by buildings and terrains;
grey: inaccessible airspace due to the minimum keep-out distance of d=10 meters from buildings
Method
Concept of geofence
In this research, we consider two types of geofence, which is a commonly accepted risk mitigation measure [1-6], in airspace availability assessment: keep-out geofence and keep-in geofence. Available airspace is defined as airspace that is not only free of static obstacles but also not affected by geofence.
Keep-out geofence is mainly used to define a protection boundary that a vehicle should not intrude. The majority of states and regulatory bodies have adopted the keep-out geofence to set a boundary for property and privacy protection.
Keep-in geofence is a similar concept to the Containment Limit (CL) of aircraft, which considers vehicle’s capability to stay within the planned trajectory under various circumstances [7-11]. This containment model is defined based on flight technical error (FTE), navigation system error (NSE), and path definition error (PDE) [12]. Experimental and theoretical studies have been conducted to examine the capability of vehicle to maintain a desired path and thus remain in the defined spatial limit under various operating conditions [13-17]. However, the lack of validated flight dynamics models and standardized risk models makes it difficult to quantify the frequency and magnitude of navigational and technical errors for all vehicle types and operating conditions [18].
<Illustration of keep-out and keep-in geofence>
Airspace availability with respect to geofence
A keep-in geofence is defined as a spherical ball to contain a vehicle, which is modeled as α-ball in the alpha shape method [19-22]. The idea of an alpha shape was first proposed by Edelsbrunner as an attempt to reconstruct the shape of a finite point set using spherical disks, or α-ball.
A vehicle is not treated as a point but assumed as a cylinder containing it. In other words, the α- ball represents the keep-in geofence of size r, or the containment limit of a vehicle. The spaces that α- ball cannot fit in are equivalent to any free airspace that a vehicle of keep-in requirement larger than r should not penetrate.
<Illustrative example of dual geofencing>
cells are color-coded according to their usability. Black cells are occupied by static obstacles, grey cells are closed by the keep-out geofence, and orange cells are closed by alpha shapes.
Results
A case study of 3-D urban area was conducted at the city-wide level and the local district level (i.e. Gangnam district).
Key findings
Airspace availability differs by altitude. Reduction in the available airspace is much greater in the lower altitude, while the effect of geofence becomes minimal at altitude of 100 m or higher.
Airspace availability differs significantly by region.
The sensitivity of airspace availability to geofence parameters indicates how complex the geospatial distribution of the available airspace is
Regions having simple and complex topology coexist throughout urban airspace
< 2D snapshots of the dual effect of keep-out and keep-in geofence of size 10 at altitude of 40 and 70 meters >
< Illustration of effect of increaing keep-in geofence from 0 to 30 at altitude of 70 meters>
Conclusion and future direction
Conclusion
The proposed framework analyzed the dual effect of geofence combinations in case studies. The overall usability was more sensitive to keep-out parameter changes than keep-in.
Tradeoffs between two geofencing methods need to be evaluated thoroughly considering various geofencing parameter combinations. If someone seeks a single threshold of geofence parameter that can apply to all cases of urban sUAV flight operations, it can either be prone to conflicts or severely restrict the amount of usable airspace and vehicle choice.
Although one can identify usable airspace in a variety of geospatial dataset for a variety of geofence parameters using our framework, the decisions on the final set of flight restriction rules require further research efforts.
Further research on integrating population risk
According to aviation safety regulations in several countries, small Unmanned Aerial System (sUAS) operations are not permitted to fly over populated areas, in order to minimize risks to population on the ground. Such conservative approach limits the use of sUAS, particularly in urban areas, where sUAS demand is expected to be concentrated.
With exponentially growing sUAS demand, such restriction may eventually be relaxed, and responsible agencies should take appropriate mitigations to address potential risk of collision between UAs and non-involved person. A simple mitigation measure is to set populated regions as no-fly zones (NFZ). Further study can be conducted to analyze the effect of restricting airspace over populated areas on airspace availability. A preliminary research outcome was presented in TRB 2020 [pdf].
< Illustration of highly populated regions in the city of Seoul between 5 am and 10 am>
References
Atkins, E. M. (2014). Autonomy as an enabler of economically-viable, beyond-line-of-sight, low-altitude UAS applications with acceptable risk. AUVSI Unmanned Systems, Orlando, FL, 12-15 May 2014, pp. 200-211.
Dill, E. T., Young, S. D., & Hayhurst, K. J. (2016). SAFEGUARD: An assured safety net technology for UAS. In: Proceedings of the IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), Sacramento, CA, 25-29 Sept. 2016, pp.1-10.
D'Souza, S., Ishihara, A., Nikaido, B., & Hasseeb, H. (2016). Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind. In: Proceedings of the IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), Sacramento, CA, 25-29 Sept. 2016, pp.1-10.
Hayhurst, K. J., Maddalon, J. M., Neogi, N. A., & Verstynen, H. A. (2015). A case study for assured containment. In: Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS). Denver, CO, 9-12 June 2015, pp. 260-269.
Johnson, M., Jung, J., Rios, J., Mercer, J., Homola, J., Prevot, T.,& Kopardekar, P. (2017). Flight Test Evaluation of an Unmanned Aircraft System Traffic Management (UTM) Concept for Multiple Beyond-Visual-Line-of-Sight Operations. In: Proceedings of the 12th USA/EUROPE Air Traffic Management R&D Seminar, Seattle, WA, 26-30 June 2017.
Johnson, S. C., Petzen, A., & Tokotch, D. (2017). Exploration of Detect-and-Avoid and Well-Clear Requirements for Small UAS Maneuvering in an Urban Environment. In: Proceedings of the 17th AIAA Aviation Technology, Integration, and Operations Conference, Denver, CO, 5-9 June 2017, pp. 3074
D’Souza, S. N. (2017). Developing a generalized trajectory modeling framework for small UAS performance in the presence of wind. AIAA Information Systems-AIAA Infotech at Aerospace, 2017, (January), 1–16.
DLR. (2017). DLR U-Space Blueprint.
Stevens, M. N., & Atkins, E. M. (2018). Geofencing in Immediate Reaches Airspace for Unmanned Aircraft System Traffic Management. In 2018 AIAA Information Systems-AIAA Infotech@ Aerospace (p. 2140).
Stevens, M. N., & Atkins, E. M. (2018). Layered geofences in complex airspace environments. In 2018 Aviation Technology, Integration, and Operations Conference (p. 3348).
Jung, J., & Nag, S. (2020). Automated Management of Small Unmanned Aircraft System Communications and Navigation Contingency. In AIAA Scitech 2020 Forum (p. 2195).
ICAO. (2010). DOC 9613: Performance based navigation operational approval handbook. International Civil Aviation Organisation.
Stepanyan, V., & Krishnakumar, K. (2017). Estimation, navigation and control of multi-rotor drones in an urban wind field. In AIAA Information Systems-AIAA Infotech at Aerospace, 2017. https://doi.org/10.2514/6.2017-0670
Stepanyan, V., & Krishnakumar, K. (2019). Input constrained m-mrac for multirotors operating in an urban environment. AIAA Scitech 2019 Forum, (January), 1–22. https://doi.org/10.2514/6.2019-1191
Stepanyan, V., Krishnakumar, K., & Ippolito, C. (2019). Coordinated turn trajectory generation and tracking control for multi-rotors operating in urban environment. AIAA Scitech 2019 Forum, (January), 1–26. https://doi.org/10.2514/6.2019-0957
Koh, C. H., Low, K. H., Li, L., Zhao, Y., Deng, C., Tan, S. K., ... & Li, X. (2018). Weight threshold estimation of falling UAVs (Unmanned Aerial Vehicles) based on impact energy. Transportation Research Part C: Emerging Technologies, 93, 228-255.
Lampton, A. K., Klyde, D. H., Prince, T., Swaney, T., & Belcastro, C. M. (2020). Toward Developing MTEs for Multirotor sUAS in Controlled Wind Conditions. In AIAA Scitech 2020 Forum (p. 1507).
Ippolito, C. A., Krishnakumar, K., Stepanyan, V., Chakrabarty, A., & Baculi, J. (2019). SAFE50 reference design study for large-scale high-density low-altitude UAS operations in urban areas. AIAA Scitech 2019 Forum, (January), 1–23.
Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of a set of points in the plane. IEEE Transactions on information theory, 29(4), 551-559.
Edelsbrunner, H., & Mücke, E. P. (1994). Three-dimensional alpha shapes. ACM Transactions on Graphics (TOG), 13(1), 43-72.
Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2000). Topological persistence and simplification. Proceedings of the 41st Annual Symposium on Foundations of Computer Science, pp.454-463.
Edelsbrunner, H. (2010). Alpha shapes—a survey. Tessellations in the Sciences, 27, 1-25.
Page updated
Google Sites
Report abuse