Programmable Synchronization Graphs for Adaptive and Fault-Tolerant Modular Miniature Robots

2026-07-08

AI Summary

This work addresses the challenge of achieving stable locomotion in large ensembles of heterogeneous micromodular robots under constraints in computation, communication, and reliability, without centralized coordination. The authors propose a programmable synchronization graph framework that models actuator–sensor pairs as graph nodes, synchronizes heterogeneous actuators through fixed intra-subgraph connections, and regulates inter-subgraph phase relationships via sparse, signed edges. This approach introduces programmable network topology as a compact control layer, enabling gait-phase programming, online adaptation, and robust synchronization even in the presence of module failures—thereby eliminating single points of failure inherent in centralized schemes. Experiments demonstrate controllable synchronization from in-phase to anti-phase patterns in systems with up to nine modules, and five-module robots executing gallop- and trot-like gaits. Compared to centralized baselines, the method reduces worst-case phase error by approximately threefold and significantly enhances fault tolerance.

Abstract

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.

Problem

• modular miniature robots
• synchronization
• fault tolerance
• sparse communication
• gait coordination

Innovation

• programmable synchronization graphs
• modular miniature robots
• sparse coupling
• fault tolerance
• phase programming