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Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion
Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through differe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751408/ https://www.ncbi.nlm.nih.gov/pubmed/36530495 http://dx.doi.org/10.3389/frobt.2022.1006786 |
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author | Manoni, Tiziano Albani, Dario Horyna, Jiri Petracek, Pavel Saska, Martin Ferrante, Eliseo |
author_facet | Manoni, Tiziano Albani, Dario Horyna, Jiri Petracek, Pavel Saska, Martin Ferrante, Eliseo |
author_sort | Manoni, Tiziano |
collection | PubMed |
description | Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability–velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree—for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions. |
format | Online Article Text |
id | pubmed-9751408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97514082022-12-16 Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion Manoni, Tiziano Albani, Dario Horyna, Jiri Petracek, Pavel Saska, Martin Ferrante, Eliseo Front Robot AI Robotics and AI Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability–velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree—for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9751408/ /pubmed/36530495 http://dx.doi.org/10.3389/frobt.2022.1006786 Text en Copyright © 2022 Manoni, Albani, Horyna, Petracek, Saska and Ferrante. 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 Manoni, Tiziano Albani, Dario Horyna, Jiri Petracek, Pavel Saska, Martin Ferrante, Eliseo Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title | Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title_full | Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title_fullStr | Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title_full_unstemmed | Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title_short | Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion |
title_sort | adaptive arbitration of aerial swarm interactions through a gaussian kernel for coherent group motion |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751408/ https://www.ncbi.nlm.nih.gov/pubmed/36530495 http://dx.doi.org/10.3389/frobt.2022.1006786 |
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