Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems
This work addresses the challenge of standardizing autonomy assessment for UAS, but it is incremental as it builds upon an existing framework.
The paper tackles the problem of measuring overall autonomy scores for robotic systems by combining non-contextual features like Autonomy Level and Component Performance, using the weighted product method to resolve ranking inconsistencies and introducing an autonomy distance representation. It applies this method to seven Unmanned Aerial Systems, obtaining absolute and relative autonomy scores.
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 overall autonomy score. We examine several methods of combining features, showing how some methods find different rankings of the same data, and we employ the weighted product method to resolve this issue. Furthermore, we introduce the non-contextual autonomy coordinate and represent the overall autonomy of a system with an autonomy distance. 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.