Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| Acceso en línea: | |
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| _version_ | 1866909233092493312 |
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| author | Hertel, Brendan Donald, Ryan Dumas, Christian Ahmadzadeh, S. Reza |
| author_facet | Hertel, Brendan Donald, Ryan Dumas, Christian Ahmadzadeh, S. Reza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_07438 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems Hertel, Brendan Donald, Ryan Dumas, Christian Ahmadzadeh, S. Reza Robotics 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. |
| title | Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2111.07438 |