The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over nonparticipant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Traffic Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off-nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.
Many beneficial civilian applications of commercial and public small unmanned aircraft systems (sUAS) in low-altitude uncontrolled airspace have been proposed and are being developed. Associated with the proliferation of civil applications for sUAS is a paradigm shift from single-UAS visual operations in restricted airspace to multi-UAS beyond visual line of sight operations with increasing use of autonomous systems and operations under increasing levels of urban development and airspace usage. Ensuring the safety of sUAS operations requires an understanding of associated current and future hazards. This is challenging for sUAS operations due to insufficient mishap (accident and incident) reporting for sUAS and the rapid growth of new sUAS applications (or use cases) that have not yet been implemented. These applications include imaging, construction, photography and video, precision agriculture, security, public safety, mapping and surveying, inspections, environmental conservation, communications, parcel delivery, and humanitarian efforts such as delivery of medical supplies in developing nations. This paper will summarize research results in the identification of: 1.) Current hazards through the analysis of sUAS mishaps; and 2.) Future hazards through the analysis of a collection of sUAS use cases. The mishaps analysis will include the identification of mishap precursors and an analysis of their individual contributions to the mishaps as well as an analysis of worst-case hazards combinations and sequences. The future hazards are identified through an assessment and categorization of use cases for sUAS, the identification of associated paradigm shifts in terms of operations and new vehicle systems (both cross-cutting and for specific use case categories), the determination of future potential hazards (relative to the vehicle, ground control station, operations, and UTM system) arising from these paradigm shifts, and future potential impacts and outcomes (relative to the vehicle, other vehicles, people, ground infrastructure, and the environment). Key findings from these analyses are also summarized. The analysis results are then used to develop a set of combined (current and future) hazards for assessing risk.
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