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Particle robotics based on statistical mechanics of loosely coupled components

机译：基于松耦合组件统计力学的粒子机器人

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摘要

Biological organisms achieve robust high-level behaviours by combining and coordinating stochastic low-level components(1-3). By contrast, most current robotic systems comprise either monolithic mechanisms(4,5) or modular units with coordinated motions(6,7). Such robots require explicit control of individual components to perform specific functions, and the failure of one component typically renders the entire robot inoperable. Here we demonstrate a robotic system whose overall behaviour can be successfully controlled by exploiting statistical mechanics phenomena. We achieve this by incorporating many loosely coupled 'particles', which are incapable of independent locomotion and do not possess individual identity or addressable position. In the proposed system, each particle is permitted to perform only uniform volumetric oscillations that are phase-modulated by a global signal. Despite the stochastic motion of the robot and lack of direct control of its individual components, we demonstrate physical robots composed of up to two dozen particles and simulated robots with up to 100,000 particles capable of robust locomotion, object transport and phototaxis (movement towards a light stimulus). Locomotion is maintained even when 20 per cent of the particles malfunction. These findings indicate that stochastic systems may offer an alternative approach to more complex and exacting robots via large-scale robust amorphous robotic systems that exhibit deterministic behaviour.
机译：生物有机体通过组合和协调随机的低水平成分来实现强大的高水平行为（1-3）。相比之下，当前大多数机器人系统都包括整体式机械（4,5）或具有协调运动的模块化单元（6,7）。这种机器人需要对单个组件进行明确的控制才能执行特定功能，并且一个组件的故障通常会使整个机器人无法操作。在这里，我们演示了一个机器人系统，该机器人系统的整体行为可以通过利用统计力学现象来成功控制。我们通过结合许多松散耦合的“粒子”来实现此目的，这些粒子无法独立运动，并且不具有个人身份或可寻址位置。在提出的系统中，允许每个粒子仅执行由全局信号进行相位调制的均匀体积振荡。尽管机器人的运动随机且缺乏直接控制其单个组件的能力，但我们仍演示了由最多两打粒子组成的物理机器人和具有多达100,000个粒子的模拟机器人，它们能够实现强大的运动，物体运输和趋光性（向光运动）刺激）。即使20％的粒子发生故障，也可以保持运动。这些发现表明，随机系统可以通过表现出确定性行为的大规模鲁棒无定形机器人系统，为更复杂，更严格的机器人提供替代方法。

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  • 来源
    《Nature》 |2019年第7748期|361-365|共5页
  • 作者

    Li Shuguang; Batra Richa; Brown David; Chang Hyun-Dong; Ranganathan Nikhil; Hoberman Chuck; Rus Daniela; Lipson Hod;

  • 作者单位

    MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA|Columbia Univ, Mech Engn Dept, Creat Machines Lab, New York, NY 10027 USA;

    Columbia Univ, Mech Engn Dept, Creat Machines Lab, New York, NY 10027 USA;

    Cornell Univ, Sch Mech & Aerosp Engn, Ithaca, NY USA;

    Cornell Univ, Sch Mech & Aerosp Engn, Ithaca, NY USA;

    Cornell Univ, Sch Mech & Aerosp Engn, Ithaca, NY USA;

    Harvard Univ, Grad Sch Design, Cambridge, MA 02138 USA|Harvard Univ, Wyss Inst Biol Inspired Engn, Cambridge, MA 02138 USA;

    MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA;

    Columbia Univ, Mech Engn Dept, Creat Machines Lab, New York, NY 10027 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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