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arXiv 2302.06709cond-mat.soft
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自驱动智能体探索多孔介质的最优运动策略

Optimal motility strategies for self-propelled agents to explore porous media

Christoph Lohrmann, Christian Holm

AI总结:

针对微型机器人在复杂多孔环境的通行需求,研究受生物启发的运动模式及带基础感知决策能力的策略在不同多孔介质中的探索效率,发现后者表现更优,可预测对应几何结构的最优运动策略。

摘要:

用于生物医学等领域的微型机器人需配备能在复杂环境中通行的运动策略。受生物微生物启发,我们在计算机模拟中重构了应用于活性杆状颗粒的运动模式,包括run-and-reverse(前进-反向)、run-and-tumble(前进-翻滚)及run-reverse-flick(前进-反向-轻弹)。我们研究了这些模式在不同孔隙度、平均孔径小至活性颗粒尺度的无序多孔环境中的高效探索能力。通过计算不同模式的有效扩散系数,我们能预测每种多孔样品几何结构对应的最优模式。我们发现,赋予智能体极基础的感知与决策能力,得到的运动模式在所有被研究的多孔样品中表现均优于受生物启发的模式。

英文摘要:

Micro-robots for, e.g., biomedical applications, need to be equipped with motility strategies that enable them to navigate through complex environments. Inspired by biological microorganisms we recreate motility patterns such as run-and-reverse, run-and-tumble or run-reverse-flick applied to active rod-like particles in silico. We investigate their capability to efficiently explore disordered porous environments with various porosities and mean pore sizes ranging down to the scale of the active particle. By calculating the effective diffusivity for the different patterns, we can predict the optimal one for each porous sample geometry. We find that providing the agent with very basic sensing and decision making capabilities yields a motility pattern outperforming the biologically inspired patterns for all investigated porous samples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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