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Aerial Swarm Defense by StringNet Herding: Theory and Experiments
This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095398/ https://www.ncbi.nlm.nih.gov/pubmed/33959638 http://dx.doi.org/10.3389/frobt.2021.640446 |
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author | Chipade, Vishnu S. Marella, Venkata Sai Aditya Panagou, Dimitra |
author_facet | Chipade, Vishnu S. Marella, Venkata Sai Aditya Panagou, Dimitra |
author_sort | Chipade, Vishnu S. |
collection | PubMed |
description | This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors. |
format | Online Article Text |
id | pubmed-8095398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80953982021-05-05 Aerial Swarm Defense by StringNet Herding: Theory and Experiments Chipade, Vishnu S. Marella, Venkata Sai Aditya Panagou, Dimitra Front Robot AI Robotics and AI This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors. Frontiers Media S.A. 2021-04-20 /pmc/articles/PMC8095398/ /pubmed/33959638 http://dx.doi.org/10.3389/frobt.2021.640446 Text en Copyright © 2021 Chipade, Marella and Panagou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Chipade, Vishnu S. Marella, Venkata Sai Aditya Panagou, Dimitra Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title | Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title_full | Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title_fullStr | Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title_full_unstemmed | Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title_short | Aerial Swarm Defense by StringNet Herding: Theory and Experiments |
title_sort | aerial swarm defense by stringnet herding: theory and experiments |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095398/ https://www.ncbi.nlm.nih.gov/pubmed/33959638 http://dx.doi.org/10.3389/frobt.2021.640446 |
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