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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2201.12900 |
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| _version_ | 1866929303427481600 |
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| author | Macktoobian, Matin Shu, Zhan Zhao, Qing |
| author_facet | Macktoobian, Matin Shu, Zhan Zhao, Qing |
| contents | In this paper, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth optimal topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of optimality criteria that our learning model successfully manages to learn. This model is an stacked ensemble whose output is the topology prediction for a particular robot. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. Applying our model to a network of 10 robots displays over 80% accuracy in the prediction of optimal topologies corresponding to various configurations of the cited network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_12900 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning Optimal Topology for Ad-hoc Robot Networks Macktoobian, Matin Shu, Zhan Zhao, Qing Robotics Machine Learning In this paper, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth optimal topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of optimality criteria that our learning model successfully manages to learn. This model is an stacked ensemble whose output is the topology prediction for a particular robot. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. Applying our model to a network of 10 robots displays over 80% accuracy in the prediction of optimal topologies corresponding to various configurations of the cited network. |
| title | Learning Optimal Topology for Ad-hoc Robot Networks |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2201.12900 |