Swarm-Sync: A distributed global time synchronization framework for swarm robotic systems

摘要

Time synchronization is a crucial service task in a distributed network. Although several works are reported in routing and medium access control of mobile wireless sensor networks (MWSNs), or for navigation in a collaborative swarm of robots, prior time synchronization is stated as one of the prior requirement. In this paper, we study the problem of time synchronization over a wireless network for a swarm robotic system. We propose a fully decentralized, energy efficient framework for global synchronization of swarm of robots. The major contribution of this work is in terms of proposing a scalable, topology independent, mobility-assisted time synchronization framework with resynchronization interval in the order of several minutes (tested up to 10 min) which we believe will accelerate development of swarm robotic systems and mobile wireless sensor networks for several human-friendly real-world applications. The proposed framework which implements time synchronization in two phases, (1) One-way time offset compensation and (2) Relative skew fingerprinting based frequency offset compensation, is flexible and can be tuned easily to suit several application scenarios. Another unique characteristic of the framework is that it utilizes only one-way messages for the time offset and frequency offset compensation. We also demonstrate that the protocol scales very well for multi-hop scenarios and that bounded synchronization error across the network can be achieved using the framework. Analysis on the suitability of our framework for dynamic environments is also presented. We also present a fair comparative analysis of our work with the predictive protocols based on techniques such as Linear regression, Linear prediction and Kalman filter and consensus based synchronization proposed for static networks. The results and analysis presented here are derived from the analytical and empirical study on mobile nodes/ robots spread over a duration of 5 months. (C) 2018 Elsevier B.V. All rights reserved.