Savoie William, Berrueta Thomas A, Jackson Zachary, Pervan Ana, Warkentin Ross, Li Shengkai, Murphey Todd D, Wiesenfeld Kurt, Goldman Daniel I
School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.
Sci Robot. 2019 Sep 18;4(34). doi: 10.1126/scirobotics.aax4316.
Robot locomotion is typically generated by coordinated integration of single-purpose components, like actuators, sensors, body segments, and limbs. We posit that certain future robots could self-propel using systems in which a delineation of components and their interactions is not so clear, becoming robust and flexible entities composed of functional components that are redundant and generic and can interact stochastically. Control of such a collective becomes a challenge because synthesis techniques typically assume known input-output relationships. To discover principles by which such future robots can be built and controlled, we study a model robophysical system: planar ensembles of periodically deforming smart, active particles-smarticles. When enclosed, these individually immotile robots could collectively diffuse via stochastic mechanical interactions. We show experimentally and theoretically that directed drift of such a supersmarticle could be achieved via inactivation of individual smarticles and used this phenomenon to generate endogenous phototaxis. By numerically modeling the relationship between smarticle activity and transport, we elucidated the role of smarticle deactivation on supersmarticle dynamics from little data-a single experimental trial. From this mapping, we demonstrate that the supersmarticle could be exogenously steered anywhere in the plane, expanding supersmarticle capabilities while simultaneously enabling decentralized closed-loop control. We suggest that the smarticle model system may aid discovery of principles by which a class of future "stochastic" robots can rely on collective internal mechanical interactions to perform tasks.
机器人运动通常由单一用途的组件(如致动器、传感器、身体部分和肢体)协调整合产生。我们假定,未来某些机器人可以利用组件及其相互作用的划分不那么清晰的系统来实现自我推进,成为由冗余且通用的功能组件组成的强大且灵活的实体,这些组件能够随机相互作用。对这样一个集合体的控制成为一项挑战,因为合成技术通常假定输入 - 输出关系是已知的。为了发现构建和控制此类未来机器人的原理,我们研究了一个模型物理系统:周期性变形的智能活性粒子(智能粒子)的平面集合。当被封闭时,这些单个不能移动的机器人可以通过随机机械相互作用进行集体扩散。我们通过实验和理论证明,通过使单个智能粒子失活可以实现这种超智能粒子的定向漂移,并利用这一现象产生内源性趋光性。通过对智能粒子活性与传输之间的关系进行数值建模,我们从小数据(单个实验试验)中阐明了智能粒子失活对超智能粒子动力学的作用。从这一映射关系中,我们证明超智能粒子可以在平面内的任何位置被外部引导,扩展了超智能粒子的能力,同时实现了分散式闭环控制。我们认为,智能粒子模型系统可能有助于发现一类未来“随机”机器人可以依靠集体内部机械相互作用来执行任务的原理。