# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 2 Literature Review
### Unmanned Traffic Management and Geofencing  
UTM has been identified as a critical capability for future small UAS operations due to their unique operating profiles at low altitude, near complex infrastructure, and likely in mixed-use airspace [3]. UTM-like concepts have been investigated by industry, government, and academia across the globe. As an example, Single European Sky ATM Research (SESAR) recommended UTM to the European Union (EU) to safely coordinate UAS [15]. Centralized and distributed UTM with airspace volumes distinguished by altitude layer was investigated to deconflict UAS traffic in Sweden [16]. UTM was modeled using a multiplayer network of nodes and airways at low-altitude airspace in Luxembourg [5]. The National Aeronautics and Space Administration (NASA) perhaps first coined the term UTM as a system architecture necessary to accommodate UAS in a low-altitude National Airspace System (NAS) layer not frequently occupied by legacy manned aircraft [3]. Representatives from industry have worked to establish adequate security protocols for managing UTM datalinks [17]. NASA, in cooperation with industry, has pursued a series of flight test events to evaluate cooperative UAS operations in beyond visual line of sight (BVLOS) conditions with a \"separation by segregation\" geofence design [11]. Airspace capacity estimation was analyzed using keep-in and keep-out geofences in [18]. A roadmap for geofence implementation in urban areas with 5G networks and blockchain was introduced in [19]. Dynamic airspace geofencing algorithms are novel to UTM. Two different but equally important perspectives (i.e., local/global) exist in geofencing designs. One perspective is a classical guidance/navigation/control (GNC) approach, where geofence layering is only generated for the individual UAS that has full knowledge of its control system. This vehicle-centered geofence perspective focuses on controlling UAS to ensure that the vehicle does not violate the geofence boundaries (given expected trajectory tracing errors) [10; 20]. In this work, each UAS monitors its real-time state vector relative to geofence boundaries to detect and react to potential breaches given uncertainties due to sensor errors and wind disturbances. Vehicle-centered geofencing research is important but does not consider properties of the operating area airspace or the ground-based environment. Geofencing has also been researched from an _airspace system_ perspective. With this viewpoint, geofences are managed by UTM to organize airspace structure and improve Situational Awareness (SA). UTM will not model individual UAS capabilities and uncertainties in detail, but it can conservatively monitor UAS travel through an approved geofence to offer impending breach warnings to the UAS and actual boundary violations to all traffic per [21]. SA is a fundamental requirement for all flight operations, autonomous or human-piloted [22; 23]; while legacy air Traffic Management (ATM) will remain distinct from UTM in the near term, advanced air mobility (AAM) supporting increasingly to fully autonomous flight will motivate the integration of ATM and UTM over the long term. UTM calls for the automation of airspace management tasks. Airspace organization and protection through geofencing can improve SA and in turn safety. Our algorithms can be integrated into both GNC (onboard) and airspace system (UTM) geofencing realizations. AAM operations, including but not limited to Urban Air Mobility (UAM), are envisioned to have higher altitudes than 400 AGL, where current UTM is designed to serve. Researchers at NASA and Uber investigated the applicability of UTM to coordinate UAM routes safely and efficiently [24]. In their case studies, \"Transit-Based Operational Volumes (TBOVs)\" were used to wrap the UAM flight path, a notion analogous to the trajectory keep-in geofence discussed in this paper.