Description:
OBJECTIVE: Develop an Unmanned Aerial System (UAS) operator test selection battery integrated into the Department of the Navy"s (DoN) existing Automated Pilot Exam (APEX) framework. DESCRIPTION: Beginning in FY12, the DoN will significantly increase its acquisition of a wide range of UAS. Despite advances in UAS capabilities, over 50% of all UAS mishaps are attributed to human factor issues, beginning with poorly defined UAS operator selection capabilities. Effective selection procedures identify individuals who possess a minimum level of qualifications and the aptitude to acquire the relevant knowledge, skills, and abilities to perform specific tasks and missions. Done properly, selection and classification procedures reduce attrition rates from training, reduce costs associated with developing user interfaces, and improve operational performance. There are currently no tools in place to select and classify candidate UAS operators based on these competencies. Preliminary research suggests that such tools should include an emphasis on assessing: spatial capabilities [1]; social and interpersonal abilities and personality traits [2,3]; executive processes, like attention management, information processing, multitasking and decision making [4,1]; and humanautonomy interactions [5,4]. Additional capabilities, with associated assessment tools, may also factor into developing an overall UAS air vehicle operator (AVO) selection capability. This topic requests technologies for selecting individuals with the aptitude to acquire the relevant knowledge, skills, and abilities to perform UAS specific tasks and missions across different platforms. Importantly, the resultant technologies should be compatible with the DoN secure web-based test delivery platform, APEX. APEX is currently used to deliver the manned Aviation Selection Test Battery worldwide. Specific products from this topic include: 1) An ontological representation of the knowledge, skills, and abilities relating to operating DoN UAS platforms according to platform and mission. 2) UAS operator selection tests and development of data collection instruments to capture test measures. 3) The ability to extend to new platforms and missions. 4) The ability to include additional positions. (Main focus is on the UAS position known as the Air Vehicle Operator (AVO) but tool should be extensible to other positions such as the Mission Payload Operator.) PHASE I: Prepare a feasibility study for developing a technology that will support the effective selection of UAS AVO. The performer will propose a prototype system and a preliminary design/architecture to include descriptions of: proposed measures; appropriate testing methodologies and technologies; and, metrics and plans for validating the resultant selection technology. A final report will be generated to include system performance metrics and plans for Phase II. Phase I should also include the processing and submission of all required human subjects use protocols. PHASE II: Phase II plans should include key component technological milestones and plans for at least one operational test and evaluation. Develop prototype system based on the preliminary design from Phase I. All appropriate testing will be performed, and a critical design review will be performed to finalize the design. Phase II deliverables will include: (1) a working prototype of the technology; (2) specifications for its development; and, (3) test data on its performance collected in one or more operational settings. PHASE III: Deploy the developed system for use in the selection of UAS operators for at least one planned DoN UAS platform. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This selection technology will have broad applications in military as well as commercial settings. UAS operations are projected to continue to grow over the next decade in military, federal, and local law enforcement applications. The ability to quickly and effectively identify those individuals with the appropriate knowledge, skills, and abilities will ensure that across these different sectors, UAS operations are conducted safely and cost effectively. REFERENCES: 1. McKinley, R.A., McIntire, L.K. and Funke, M.A. 2011."Operator Selection for Unmanned Aerial Systems: Comparing Video Game Players and Pilots."Aviation Space Environmental Medicine 82:63542. http://www.ncbi.nlm.nih.gov/pubmed/21702315. 2. Kay, G., Dolgin, D., Wasel, B., Langelier, M. and Hoffman, C. 1999."Identification of the Cognitive, Psychomotor, and Psychosocial Skill Demands of Uninhabited Combat Aerial Vehicle (UCAV) Operators."Naval Air Warfare Center Report. Accessed: February 12, 2012. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA368578. 3. Carretta, T. R. and Ree, M. J. 2003."Pilot Selection Methods."In B. H. Kantowitz, P. S. Tsang, and M.A. Vidulich (Eds.). Human Factors in Transportation: Principles and Practices of Aviation Psychology (pp. 357-396). Mahwah, NJ: Erlbaum. 4. Squire, P. N. and Parasuraman, R. 2010."Effects of Automation and Task Load on Task Switching During Human Supervision of Multiple Semi-Autonomous Robots in a Dynamic Environment."Ergonomics 53: 8, 951-961. http://archlab.gmu.edu/People/rparasur/Documents/SquireParaErgo2010.pdf. 5. McCarley J.S. and Wickens C.D. 2005."Human Factors Implications of UAVs in the National Airspace."Atlantic City, NJ: Federal Aviation Administration, Department of Transportation; Report No. AHFD-0505/FAA-0501. http://www.tc.faa.gov/logistics/Grants/pdf/2004/04-G-032.pdf.
