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arXiv 2009.05710cond-mat.softcs.DCcs.RO
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利用机械诱导相变对活性黏性颗粒物质进行编程

Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

Georgia Institute of Technology(佐治亚理工学院);Claremont McKenna College(克莱蒙特·麦肯纳学院);Arizona State University(亚利桑那州立大学)

Shengkai Li, Bahnisikha Dutta, Sarah Cannon, Joshua J. Daymude, Ram Avinery, Enes Aydin, Andréa W. Richa, Daniel I. Goldman, Dana Randall

AI总结:

提出结合自组织粒子理论与无数字计算、少传感的活性黏性颗粒机器人实验,通过机械诱导的吸引相变实现聚集、分散及集体输运杂质。

摘要:

活性物质物理学与群体机器人学为研究和控制由内源驱动的集合体提供了强有力的工具。在宏观尺度上,控制群体通常需要大量内存、处理能力和协调,而这些在微观尺度上无法获得,例如对于胶体机器人而言;这类机器人可能有助于对抗疾病、制造智能纺织品以及设计纳米计算机。为了发展能够利用相互作用物理、因而可跨尺度应用的原理,我们采取了一种双管齐下的方法:一是对自组织粒子系统进行理论抽象,二是构建一个由活性黏性颗粒物质组成的实验机器人系统,该系统刻意不具备数字电子计算和通信能力,使用最少(或完全不使用)的传感与控制,以检验理论预测。我们研究了聚集、分散和集体输运问题。正如理论所预测的,随着代表粒子间吸引力的参数增大,机器人从分散相转变为聚集相,形成一个致密、紧凑的集体。处于聚集状态时,该集体能够输运其环境中的非机器人“杂质”,从而执行一项由该转变背后的物理所驱动的涌现任务。这些结果表明,算法设计与活性物质机器人物理学之间富有成效的相互作用,能够带来新的非平衡物理,以及无需复杂算法或能力即可对集体进行编程的原理。

英文摘要:

Active matter physics and swarm robotics have provided powerful tools for the study and control of ensembles driven by internal sources. At the macroscale, controlling swarms typically utilizes significant memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that that can leverage physics of interactions and thus can be utilized across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control, to test theoretical predictions. We consider the problems of aggregation, dispersion, and collective transport. As predicted by the theory, as a parameter representing interparticle attraction increases, the robots transition from a dispersed phase to an aggregated one, forming a dense, compact collective. When aggregated, the collective can transport non-robot "impurities" in their environment, thus performing an emergent task driven by the physics underlying the transition. These results point to a fruitful interplay between algorithm design and active matter robophysics that can result in new nonequilibrium physics and principles for programming collectives without the need for complex algorithms or capabilities.

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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