发表机构
UC San Diego（加州大学圣迭戈分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出气动神经元软体执行器，通过充气-触发机制构建自激励网络，实现无需电子控制器的软体机器人节律运动，并揭示其动力学分岔规律。
AI 中文摘要
动物通过分布式的神经回路协调其运动，但软体机器人通常仍依赖外部集中式电子设备进行控制。构建无需集中式电子控制器且能对环境保持响应的软体机器人，仍是软体机器人领域的前沿挑战。在这项工作中，我们引入了一种受生物神经回路泄漏积分-触发模型启发的软体机器人控制架构。气动神经元（Pneu-ron）是一种软体执行器，将能量转换、逻辑和驱动统一于一个组件中。每个模块将低沸点流体（LBF）、加热器和机械开关组合成一个自激励单元。加热使LBF沸腾，导致模块充气，并触发相邻模块的兴奋和抑制，这一过程我们称之为“充气-触发”。当相互连接成兴奋-抑制环时，Pneu-rons产生稳定的顺序振荡，其频率由材料动力学和环境条件决定。通过利用Pneu-rons的充气进行驱动，这些网络可以驱动软体机器人的振荡运动。Pneu-ron网络在机械负载和热变化下维持振荡，通过材料物理而非计算来适应。对这些网络的动力学建模揭示了一个无量纲分岔图，该图决定了网络的振荡行为。将逻辑和驱动编码到材料级模块中，为自适应、无电子控制器的软体机器人提供了新途径。
英文摘要
Animals coordinate their movements through distributed neural circuits, but soft robots still typically depend on external, centralized electronics for control. Building soft robots that operate without centralized electronic controllers while remaining responsive to their environment remains a frontier challenge in soft robotics. In this work we introduce a soft-robot control architecture inspired by leaky integrate-and-fire models of biological neural circuits. The Pneumatic neuron (Pneu-ron) is a soft actuator that unifies energy conversion, logic, and actuation in one component. Each module combines a low-boiling-point fluid (LBF), a heater, and a mechanical switch into a self-excitable unit. Boiling the LBF inflates the module and triggers excitation and inhibition of adjacent modules in a process we call "inflate-and-fire". When interconnected into excitatory-inhibitory rings, Pneu-rons generate stable, sequential oscillations whose frequency emerges from the material dynamics and environmental conditions. By harnessing the inflation of Pneu-rons for actuation these networks can drive oscillatory locomotion of soft robots. Pneu-ron networks sustain oscillation under mechanical load and thermal variations, adapting through material physics rather than computation. Dynamical modeling of these networks reveals a dimensionless bifurcation diagram that dictates the network's oscillatory behavior. Encoding logic and actuation into material-level modules presents a new avenue for adaptive, electronics controller-free, soft robots.
