Scenario-based Learning for Airspace Management and Deconfliction (SLAMD)

ABSTRACT: Difficult under typical conditions, airspace management and deconfliction becomes even more difficult in a mixed manned/unmanned environment. As UASs gain capabilities to operate in more complex environmental conditions, the challenges associated with mixed airspace management and deconfliction will become more apparent and the chances of fatal mishaps will increase. Current training for UAS operators does not teach airspace management concepts. While airspace management is well known to controllers and manned system pilots, understanding the nuances associated with interoperating with UASs is not the focus of their training. To meet this need, the Aptima team will develop SLAMD (Scenario-based Learning for Airspace Management and Deconfliction), a flexible system enabling its users to translate real-world airspace scenarios into immersive, high-fidelity training events. Using mishap logs as the basis for training scenarios, SLAMD will use real-world events as the basis for instructional scenarios, creating a flexible scenario envelope within which trainees can learn critical instructional concepts. Built on the DDD simulation environment, SLAMD will provide automated assessment during the training exercise for review during debrief. The consolidated product of SLAMD will provide an end to end training generation, simulation, and assessment system to enhance performance of operators involved with the airspace management problem. BENEFIT: The SLAMD training environment will provide users with a flexible scenario envelope by which they can leverage real-world scenarios and mishaps to generate rich training content. SLAMD will leverage currently existing technology, such as Aptima"s DDD, to substantially advance the quality of this product at the conclusion of the SBIR period. In addition, SLAMD will allow for rapid creation of constructive entities to increase the fidelity of the training simulation. Lastly, SLAMD will implement performance measurement technology to assess whether specific training objectives are being met and, if not, to provide feedback to trainees for enhancing their learning experience. The commercial applications for SLAMD are promising. In addition to targeting other DoD UAS operators and airspace controllers, we will target NASA, FAA, Commercial Airlines through the Next Generation Air Transportation System, and state and federal emergency management agencies.