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.

翻译：测量机器人系统的整体自主性分数需要综合系统的一组相关方面和特征，这些方面和特征可能以不同单位、定性形式或相互矛盾的方式被度量。本文基于现有的非情境化自主性框架，该框架通过测量并组合系统的自主性等级与组件性能作为整体自主性分数。我们研究了多种特征组合方法，展示了某些方法如何对相同数据得出不同的排序结果，并采用加权乘积法解决该问题。此外，我们引入了非情境化自主性坐标，通过自主性距离表征系统的整体自主性。我们将该方法应用于七种无人机系统，获得了它们的绝对自主性分数以及相对于最优系统的相对分数。