Interoperability for Autonomy
2024
- 65Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage65
- Downloads59
- Abstract Views6
Artifact Description
Autonomous systems aim to augment human capabilities with machine-based decision-making in the absence of a user. Ideally, autonomy hardware and software would be modular, having the ability to swap components in and out as needed based on necessary capabilities. However, many legacy systems in use utilize proprietary software with specific standards and components, reducing the system’s ability to be interoperable. Currently, the literature’s definition of interoperability is vague and often mistaken for other similar terms. We distinguish the uniqueness of interoperability and codify it through a taxonomy. Next, we extend this framework to understand autonomy and its hardware/software components through a proposed unified autonomy stack. We then evaluate the similarity between four autonomy architectures based on 29 stack components that are later presented in the “interchangeability matrix.” Thus, we demonstrate the necessity to unify autonomy hardware/software under the proposed taxonomy in the development of future autonomous systems.
Bibliographic Details
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