基于ROS2的集中式多智能体机器人系统通信处理定量分析

Lukas Johannes Dust, Emil Persson, Mikael Ekström, S. Mubeen, Emmanuel Dean
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引用次数: 2

摘要

多代理机器人系统,特别是动态环境中与人类交互的移动机器人,例如,在生产环境中提供协助,在过去几年中已经引起了越来越多的兴趣。为了更好地理解高节点负载网络中的ROS2通信,本文研究了使用ROS2的集中式多智能体机器人系统中多个机器人到单个跟踪节点的通信处理。因此,本文使用ROS2 Galactic对两种发布者-订阅者通信架构进行了定量分析,并对DDS供应商(CycloneDDS、FastDDS和GurumDDS)进行了比较研究。考虑的体系结构是多对一方法,其中多个机器人通过一个主题与中心节点通信,以及一对一通信方法,其中多个机器人通过特定主题与中心节点通信。在整个工作过程中,在一台计算机上为不同的DDS供应商模拟了不同发布速率下机器人数量的增加。进一步的模拟使用CycloneDDS的分布式设置完成。仿真结果表明,随着节点数量的增加,一对一方法的平均数据年龄和数据缺失率明显低于多对一方法。CycloneDDS在系统启动时的崩溃和响应时间方面表现得最稳健,而FastDDS在数据老化方面表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative analysis of communication handling for centralized multi-agent robot systems using ROS2
Multi-agent robot systems, specifically mobile robots in dynamic environments interacting with humans, e.g., assisting in production environments, have seen an increased interest over the past years. To better understand the ROS2 communication in a network with a high load of nodes, this paper investigates the communication handling of multiple robots to a single tracking node for centralized multi-agent robot systems using ROS2. Thereore, a quantitative analysis of two publisher-subscriber communication architectures and a comparative study between DDS vendors (CycloneDDS, FastDDS and GurumDDS) using ROS2 Galactic is performed. The architectures of consideration are a many-to-one approach, where multiple robots communicate to a central node over one topic, and the one-to-one communication approach, where multiple robots communicate over particular topics to a central node. Throughout this work, the increase in the number of robots at different publishing rates is simulated on a single computer for the different DDS vendors. A further simulation is done using a distributed setup with CycloneDDS. The simulations show that with an increase in the number of nodes, the average data age and the data miss ratio in the one-to-one approach were significantly lower than in the many-to-one approach. CycloneDDS was shown as the most robust regarding crashes and response time under system launch, while FastDDS showed better results regarding the data ageing.
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