Li Shengkai, Dutta Bahnisikha, Cannon Sarah, Daymude Joshua J, Avinery Ram, Aydin Enes, Richa Andréa W, Goldman Daniel I, Randall Dana
School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Sci Adv. 2021 Apr 23;7(17). doi: 10.1126/sciadv.abe8494. Print 2021 Apr.
At the macroscale, controlling robotic swarms typically uses substantial 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 can leverage physical interactions and thus be used 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. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot "impurities," thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.
在宏观尺度上，控制机器人群体通常需要大量内存、处理能力以及微观尺度（例如对于胶体机器人）所不具备的协调能力，这对于对抗疾病、制造智能纺织品和设计纳米计算机可能会很有用。为了开发能够利用物理相互作用并因此可跨尺度使用的原理，我们采用了双管齐下的方法：对自组织粒子系统进行理论抽象，以及构建一个由活性粘性颗粒物质组成的实验机器人系统，该系统有意不具备数字电子计算和通信功能，仅使用最少（或不使用）传感和控制。正如理论所预测的那样，随着粒子间吸引力的增加，聚集体从分散相转变为致密相。聚集时，聚集体可以运输非机器人“杂质”，从而执行由转变背后的物理原理驱动的涌现任务。这些结果揭示了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，这可以产生无需复杂算法或能力即可对聚集体进行编程的原理。
