Can flocking aid the path planning of microswimmers in turbulent flows?
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916494089125888 |
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| author | Gupta, Akanksha Alageshan, Jaya Kumar Kiran, Kolluru Venkata Pandit, Rahul |
| author_facet | Gupta, Akanksha Alageshan, Jaya Kumar Kiran, Kolluru Venkata Pandit, Rahul |
| contents | We show that flocking of microswimmers in a turbulent flow can enhance the efficacy of reinforcement-learning-based path-planning of microswimmers in turbulent flows. In particular, we develop a machine-learning strategy that incorporates Vicsek-model-type flocking in microswimmer assemblies in a statistically homogeneous and isotropic turbulent flow in two dimensions (2D). We build on the adversarial-reinforcement-learning of Ref.~\cite{alageshan2020machine} for non-interacting microswimmers in turbulent flows. Such microswimmers aim to move optimally from an initial position to a target. We demonstrate that our flocking-aided version of the adversarial-reinforcement-learning strategy of Ref.~\cite{alageshan2020machine} can be superior to earlier microswimmer path-planning strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15902 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can flocking aid the path planning of microswimmers in turbulent flows? Gupta, Akanksha Alageshan, Jaya Kumar Kiran, Kolluru Venkata Pandit, Rahul Fluid Dynamics We show that flocking of microswimmers in a turbulent flow can enhance the efficacy of reinforcement-learning-based path-planning of microswimmers in turbulent flows. In particular, we develop a machine-learning strategy that incorporates Vicsek-model-type flocking in microswimmer assemblies in a statistically homogeneous and isotropic turbulent flow in two dimensions (2D). We build on the adversarial-reinforcement-learning of Ref.~\cite{alageshan2020machine} for non-interacting microswimmers in turbulent flows. Such microswimmers aim to move optimally from an initial position to a target. We demonstrate that our flocking-aided version of the adversarial-reinforcement-learning strategy of Ref.~\cite{alageshan2020machine} can be superior to earlier microswimmer path-planning strategies. |
| title | Can flocking aid the path planning of microswimmers in turbulent flows? |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2411.15902 |