A ConOps derived UAS safety risk model
Bibliographic Data
| ID | 12597151 |
|---|---|
| Authors | James T Luxhøj (Industrial & Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA, corresponding author), William Joyce (0000-0002-3782-1641, Industrial & Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA), Carl A Luxhoj (United States Coast Guard), Carl Luxhøj (U.S. Coast Guard , New Orleans, LA, USA) |
| Year | 2017 |
| Volume | 24 |
| Issue | 7 |
| Pages | 796-818 |
| Publication date | 2017-12-13 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Risk Research (JOURNAL) |
| Journal identifiers | ISSN: 1366-9877 • E-ISSN: 1466-4461 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/13669877.2017.1409253 |
| OpenAlex | W2771954472 |
| Language | EN |
| References cited | 10 |
The purpose of NASA’s Unmanned Aircraft Systems (UAS) Integration in the National Airspace System project is to support the FAA with research that examines the risks involved with using UAS for commercial operations. The safety risk model described in this paper aims to provide such research and is derived from a NASA Concept of Operations (ConOps). A ConOps is a document that describes a specific commercial operation for an aircraft and presents the operation’s logistics, its associated environment, plausible scenarios, safety and performance considerations of the operation, and a possible plan for the execution of the operation. The specific operation defined in the studied ConOps focuses on a UAS administering a ‘targeted aerial application’ of agricultural chemicals to small, pre-determined areas of crops in large fields. Applying UAS to agriculture in this manner is very realistic since it is a relatively minimal risk operation compared to other potential UAS commercial operations and presents significant value to the agricultural community
Aeronautics · Biology · Business · Operations research · Plan (archaeology · Risk analysis (engineering · Systems engineering · Bayesian Modeling and Causal Inference · Computer Science · Engineering · Fault Detection and Control Systems · Risk and Safety Analysis
| Highly cited | No |
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