发表机构
Department of Mechanical Engineering, Bilkent University; International Iberian Nanotechnology Laboratory (INL)（巴尔坎大学机械工程系; 国际伊比拉纳米技术实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究模块化微型机器人协调难题，提出可编程同步图框架，通过图耦合编码运动协调，实现同步、相位调整、容错及适应，还能减轻耦合负担，降低相位误差，确立其为紧凑控制层。
AI 中文摘要
模块化微型机器人可在受限环境中提供可扩展功能，但在计算、通信和可靠性有限时协调多个不完美模块仍很困难。本文引入可编程同步图框架，其中每个执行器-传感器对表示为网络节点，运动协调通过图耦合编码。固定子图内链接同步异构执行器组，少量带符号子图间链接编程组间相位关系。在多达九个模块的物理机器人集体中，图耦合驱动同步出现，带符号链接调整相位差，地面实验在五模块机器人组件中产生类似疾驰和小跑的接触模式。用稀疏d-正则拓扑取代密集全对全耦合可保持同步并减轻耦合负担。相同图表示还捕获容错能力，增加图度可增加失步前容忍的模块停用数量。最后，一种上置信界边缘选择算法学习驱动系统趋向目标相位状态的子图间链接。在单独的停用基准测试中，基于图的控制器避免了集中式领导者-跟随者控制中观察到的特定领导者故障模式，并将最坏情况下的相位误差降低了约三倍。这些结果将可编程网络拓扑确立为模块化微型机器人步态相位编程、在线适应和对单元损失鲁棒性的紧凑控制层。
英文摘要
Modular miniature robots could provide scalable function in constrained environments, but coordinating many imperfect modules remains difficult when computation, communication and reliability are limited. A central robotics challenge is to coordinate many actuator-sensor modules without assigning a privileged leader, prescribing a fixed gait template, or relying on dense communication. Here we introduce a programmable synchronization-graph framework for modular miniature robots in which each actuator-sensor pair is represented as a network node and locomotor coordination is encoded through graph coupling. Fixed intra-subgraph links synchronize heterogeneous actuator groups, whereas a small number of signed inter-subgraph links program phase relationships between groups. In physical robot collectives with up to nine modules, graph coupling drives the emergence of synchronization, signed links tune the phase difference from in-phase to out-of-phase motion, and floor experiments produce gallop-like and trot-like contact patterns in a five-module robot assembly. Replacing dense all-to-all coupling with sparse d-regular topologies preserves synchronization while reducing the coupling burden. The same graph representation also captures fault tolerance: increasing graph degree increases the number of module deactivations tolerated before desynchronization. Finally, an upper-confidence-bound edge-selection algorithm learns inter-subgraph links that drive the system toward target phase states. In a separate deactivation benchmark, the graph-based controller avoids the leader-specific failure mode observed in centralized leader-follower control and reduces worst-case phase error by about threefold. These results establish programmable network topology as a compact control layer for gait phase programming, online adaptation and robustness to unit loss in modular miniature robots.
