论文标题
肌肉灵感的柔性机械逻辑体系结构用于胶体机器人技术
Muscle-inspired flexible mechanical logic architecture for colloidal robotics
论文作者
论文摘要
通过扩展或收缩来响应外部刺激的材料提供了一条转导路线，该途径集成了直接由刺激供电的传感和驱动。这促使我们使用这些材料可以变成任意配置的材料来构建胶体尺度机器人。为了在机器人系统中智能使用全球刺激，需要将计算能力纳入其中。面临的挑战是设计一种紧凑，物质不可知论，稳定在随机力下的建筑，并可以采用刺激反应性材料。我们提出了一种结构，该体系结构使用使用类似肌肉的响应的机械门来计算组合逻辑 - 作为电路信号 - 逻辑电路的其他好处是物理灵活的，并且能够改造为任意机器人体。我们数学分析了栅极几何形状，并讨论对信号尺寸和幅度的给定要求进行调整。我们使用布朗动力学模拟在胶体尺度上验证设计的功能和稳定性。我们还使用3D打印模型演示了栅极设计。最后，我们模拟了一个完整的机器人，该机器人折叠成俄罗斯方面的形状。
Materials that respond to external stimuli by expanding or contracting provide a transduction route that integrates sensing and actuation powered directly by the stimuli. This motivates us to build colloidal scale robots using these materials that can morph into arbitrary configurations. For intelligent use of global stimuli in robotic systems, computation ability needs to be incorporated within them. The challenge is to design an architecture that is compact, material agnostic, stable under stochastic forces and can employ stimuli-responsive materials. We present an architecture that computes combinatorial logic using mechanical gates that use muscle-like response - expansion and contraction - as circuit signal with additional benefits of logic circuitry being physically flexible and able to be retrofit to arbitrary robot bodies. We mathematically analyze gate geometry and discuss tuning it for the given requirements of signal dimension and magnitude. We validate the function and stability of the design at the colloidal scale using Brownian dynamics simulations. We also demonstrate the gate design using a 3D printed model. Finally, we simulate a complete robot that folds into Tetris shapes.
