Obstacle-aware navigation of smart microswimmers in a turbulent flow
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911586461941760 |
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| author | Gajendragad, Vaishnavi Gupta, Akanksha Padhan, Nadia Bihari Pandit, Rahul |
| author_facet | Gajendragad, Vaishnavi Gupta, Akanksha Padhan, Nadia Bihari Pandit, Rahul |
| contents | Microswimmers in turbulent flows often navigate complex, heterogeneous, and obstacle-rich environments, where they exhibit intricate behaviors such as trapping at and escape from obstacles. We generalize recent $\mathcal{Q}-$learning methods of J.K. Alageshan \textit{et al.} [Phys.Rev.E \textbf{101}, 043110 (2020)] and A. Gupta \textit{et al.} [Physics of Fluids \textbf{37}, 045107 (2025)] developed for non-interacting microswimmers that aim to move optimally from an initial position to a target, to account for the additional complication of an obstacle in the flow. We begin by considering one circular obstacle in forced two-dimensional (2D) Navier-Stokes turbulence in which the energy spectrum displays a forward cascade. We employ the volume-penalization method to introduce this obstacle within our doubly periodic simulation domain. We augment our adversarial $\mathcal{Q}-$learning Refs.~\cite{Alageshan_2020,Akanksha_2025} by suppressing the tendency of microswimmers to get trapped in stagnation points in the vicinity of the obstacle. We demonstrate that smart microswimmers ($SS$), which adopt our obstacle-aware adversarial $\mathcal{Q}-$learning strategy, outperform both naïve swimmers ($NS$) and surfers ($SuS$). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21223 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Obstacle-aware navigation of smart microswimmers in a turbulent flow Gajendragad, Vaishnavi Gupta, Akanksha Padhan, Nadia Bihari Pandit, Rahul Fluid Dynamics Microswimmers in turbulent flows often navigate complex, heterogeneous, and obstacle-rich environments, where they exhibit intricate behaviors such as trapping at and escape from obstacles. We generalize recent $\mathcal{Q}-$learning methods of J.K. Alageshan \textit{et al.} [Phys.Rev.E \textbf{101}, 043110 (2020)] and A. Gupta \textit{et al.} [Physics of Fluids \textbf{37}, 045107 (2025)] developed for non-interacting microswimmers that aim to move optimally from an initial position to a target, to account for the additional complication of an obstacle in the flow. We begin by considering one circular obstacle in forced two-dimensional (2D) Navier-Stokes turbulence in which the energy spectrum displays a forward cascade. We employ the volume-penalization method to introduce this obstacle within our doubly periodic simulation domain. We augment our adversarial $\mathcal{Q}-$learning Refs.~\cite{Alageshan_2020,Akanksha_2025} by suppressing the tendency of microswimmers to get trapped in stagnation points in the vicinity of the obstacle. We demonstrate that smart microswimmers ($SS$), which adopt our obstacle-aware adversarial $\mathcal{Q}-$learning strategy, outperform both naïve swimmers ($NS$) and surfers ($SuS$). |
| title | Obstacle-aware navigation of smart microswimmers in a turbulent flow |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2603.21223 |