Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems

Measuring an overall autonomy score for a robotic system requires the combination of a set of relevant aspects and features of the system that might be measured in different units, qualitative, and/or discordant. In this paper, we build upon an existing non-contextual autonomy framework that measures and combines the Autonomy Level and the Component Performance of a system as an overall autonomy score. We examine several methods of combining features, showing how some methods find different rankings from the same data. We discuss resolving this issue by employing the weighted product method. Furthermore, we introduce two new means for representing relative and absolute autonomy score, namely, the autonomy coordinate system and the autonomy distance which represents the overall autonomy of a system. We apply our method to a set of seven Unmanned Aerial Systems (UAS) and obtain their absolute autonomy score as well as their relative score with respect to the best system.