Experimental Study of Decentralized Robot Network Coordination

TL;DR

This paper experimentally evaluates decentralized synchronization algorithms in multi-robot networks, demonstrating how parameter adjustments influence synchronization efficiency and stability, with implications for cooperative robotics tasks.

Contribution

It improves and experimentally tests decentralized synchronization algorithms for robotic networks, showing how parameter tuning affects performance and stability.

Findings

Parameter adjustments impact synchronization time and stability

Different methods yield varying synchronization results

Algorithms show promise for future cooperative robotics applications

Abstract

Synchronization and desynchronization in networks is a highly studied topic in many electrical systems, but there is a distinct lack of research on this topic with respect to robotics. Creating an effective decentralized synchronization algorithm for a robotic network would allow multiple robots to work together to achieve a task and would be able to adapt to the addition or loss of robots in real-time. The purpose of this study is to improve algorithms implemented developed by the authors for this purpose and experimentally evaluate these methods. The most effective algorithm for synchronization and desynchronization found in a former study were modified to improve testing and vary its methods of calculation. A multi-robot platform composed of multiple Roomba robots was used in the experimental study. Observation of data showed how adjusting parameters of the algorithms affected both the time to reach a desired state of synchronization or desynchronization and how the network maintained this state. Testing three different methods on each algorithm showed differing results. Future work in cooperative robotics will likely see success using these algorithms to accomplish a variety of tasks.