首页 /研究 /Enhancing the Resilience of ROS 2-based Multi-Robot Systems with Kubernetes: A Case Study on UWB-Based Relative Positioning
SWARM

Enhancing the Resilience of ROS 2-based Multi-Robot Systems with Kubernetes: A Case Study on UWB-Based Relative Positioning

Jiaqiang Zhang, Xianjia Yu, Tomi Westerlund

发表年份
2025
引用次数
1
访问权限
开放获取

摘要

ROS (Robot Operating System) has become the de facto standard in robotics research and development, with ROS 2 in particular offering enhanced support for real-time communication, distributed systems, and scalable multi-robot applications. These capabilities have driven its widespread adoption across academia, industry, and the open-source community. However, deploying ROS 2 applications across heterogeneous hardware platforms remains a complex task—especially in scenarios that require tightly coordinated multi-agent systems. In such cases, the failure of a single agent can propagate disruptions throughout the system. A representative example is Ultra-wideband (UWB)-based multi-robot relative localization, where inter-robot dependencies are essential for maintaining accurate relative positioning. While Kubernetes offers powerful features for automated deployment and orchestration, its integration with ROS 2 has not yet been thoroughly evaluated within the context of specific robotic applications. This paper addresses this gap by integrating Kubernetes with ROS 2 in a UWB-based multi-robot localization system, using UWB ranging error mitigation as a representative application. An edge cluster comprising five NVIDIA Jetson Nano devices and one laptop is orchestrated using Kubernetes, with a Jetson Nano node mounted on each robot. We deploy Long Short-Term Memory (LSTM)-based error mitigation modules on the edge nodes and systematically induce failures in various combinations of these modules. The system’s resilience and robustness are then assessed by analyzing position errors under different failure scenarios.

关键词

Resilience (materials science)RobotComputer scienceBusinessArtificial intelligencePhysics

相关论文

查看 SWARM 分类全部论文