Metrics, Schmetrics! How The Heck Do You Determine A UAV's Autonomy Anyway
Abstract
The recently released DoD Unmanned Aerial Vehicles Roadmap discusses advancements in UAV autonomy in terms of autonomous control levels (ACL). The ACL concept was pioneered by researchers in the Air Force Research Laboratory's Air Vehicles Directorate who are charged with developing autonomous air vehicles. In the process of developing intelligent autonomous agents for UAV control systems we were constantly challenged to "tell us how autonomous a UAV is, and how do you think it can be measured?" Usually we hand-waved away the argument and hoped the questioner will go away since this is a very subjective, and complicated, subject, but within the last year we've been directed to develop national intelligent autonomous UAV control metrics - an IQ test for the flyborgs, if you will. The ACL chart is the result. We've done this via intense discussions with other government labs and industry, and this paper covers the agreed metrics (an extension of the OODA - observe, orient, decide, and act - loop) as well as the precursors, "dead-ends", and out-and-out flops investigated to get there.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2002
- Accession Number
- ADA515926
Entities
People
- Bruce T. Clough
Organizations
- Air Force Research Laboratory