Metrics , Schmetrics ! How The Heck Do You Determine A UAV ’ s Autonomy Anyway ?

So, they had to be simple, apply to a broad range of systems, and yet exhibit good resolution. Obviously a simple task, but first let’s look at what it means to be autonomous. he recently released DoD Unmanned Aerial Vehicles map [9] discusses advancements in UAV autonomy in of autonomous control levels (ACL). The ACL concept pioneered by researchers in the Air Force Research ratory’s Air Vehicles Directorate who are charged with loping autonomous air vehicles. In the process of loping intelligent autonomous agents for UAV control ms we were constantly challenged to “tell us how omous a UAV is, and how do you think it can be ured...” Usually we hand-waved away the argument and d the questioner will go away since this is a very subjective, complicated, subject, but within the last year we’ve been ted to develop national intelligent autonomous UAV control cs an IQ test for the flyborgs, if you will. The ACL chart result. We’ve done this via intense discussions with other rnment labs and industry, and this paper covers the agreed cs (an extension of the OODA observe, orient, decide, and loop) as well as the precursors, “dead-ends”, and out-andlops investigated to get there. 2. Quick Difference Between Autonomous and Automatic (our definition) Many people don’t realize that there is a significant difference between the words autonomous and automatic. Many news and trade articles use these words interchangeably. Automatic means that a system will do exactly as programmed, it has no choice. Autonomous means that a system has a choice to make free of outside influence, i.e., an autonomous system has free will. For instance, let’s compare functions of an automatic system (autopilot) and an autonomous guidance system: •  Stay on course chosen.  Decide which course to take, then stay on it. eywords: autonomy metrics, machine intelligence ics, UAV, autonomous control Example: a cruise missile is not autonomous, but automatic since all choices have been made prior to launch. Background p levels of the US Department of Defense an effort been initiated to coordinate researchers across the ices and industry in meeting national goals in fixedvehicle development. The Fixed-Wing Vehicle ative (FWV) has broad goals across numerous vehicle nologies. One of those areas is mission management AVs. Our broad goal is to develop the technology ing UAVs to replace human piloted aircraft for any eivable mission. This implies that we have to give s some level of autonomy to accomplish the ions. One of the cornerstones of the FWV process is stablishment of metrics so one know that a goal is hed, but what metrics were available for measuring autonomy? Our research, in conjunction with stry, determined that there was not any sort of metric e desired. Thus we set out to define our own [Note 3. We Need To Measure Autonomy, Not Intelligence For some reason people tend to equate autonomy to intelligence. Looking through the proceedings of the last NIST Intelligent Systems Workshop there are several papers which do this, and in fact, the entire conference sets the tone that “intelligence is autonomy” . They are not the same. Many stupid things are quite autonomous (bacteria) and many very smart things are not (my 3 year old daughter seemingly most of the time). Intelligence (one of a myriad of definitions) is the capability of discovering knowledge and using it to do something.  • the ability to generate one’s own purposes without any instruction from outside (L. Fogel) what characteristics should these metrics have? We ded that they needed to be: • having free will (B. Clough) What we want to know is how well a UAV will do a task, or better yet, develop tasks to reach goals, when we’re not around to do it for the UAV. We really don’t care how intelligent it is, just that it does the job assigned. Therefore, intelligence measures tell us little. So, although we could talk about the Turing Test  and other intelligence metrics, that is not what we wanted. Easily visualized such that upper management could grasp the concepts in a couple of briefing slides. Broad enough to measure past, present and future autonomous system development. Have enough resolution to easily track impact of technological program investments.