为机器人团队设计的容器化架构,解决多设备协同开发难题。
CSAR: Containerized System Architecture for Robotics

- 用容器技术隔离依赖,实现跨设备一致运行环境。
- 实测提升资源利用率,支持边缘3D建图与GPU语义映射任务。
- 适合需要协作开发、共享硬件的科研与工业机器人团队。
机器人应用日益依赖嵌入式设备、边缘服务器和云资源组成的分布式计算架构。这种演进结合机器人项目的协作特性,使系统开发、集成、部署和长期运维变得极为复杂。实际中,多用户机器人软件团队常面临依赖冲突、兼容性差、结果不可复现、专用硬件共享困难及异构环境部署等问题。本文提出CSAR(Containerized System Architecture for Robotics),一种面向机器人团队和边缘-云连续体的容器中心架构。CSAR融合LXC/LXD系统容器化、ROS 2/DDS通信机制与三层边缘基础设施,将计算组织为贴近硬件、持久运行的环境,与实验性工作负载解耦。通过基础设施核心、平台与多用户编排、计算与加速层,实现强隔离、可控资源共享与拓扑感知网络。在学术实验室真实部署中,通过边缘卸载3D SLAM与GPU加速语义映射等用例验证,结果表明CSAR简化了软件集成,提升了资源利用率,支持安全原型设计与可复现、协作的实验。本文实现包括部署模板、配置文件与文档,已开源于https://github.com/goyoambrosio/CSAR。
原文摘要 · Abstract (English)
Robotic applications increasingly rely on distributed computational infrastructures that combine embedded devices, edge servers, and cloud resources. This evolution, together with the collaborative nature of robotics projects, has made the development, integration, deployment, and long-term operation of robotic systems significantly more complex. In practice, multi-user robotics software teams face persistent challenges related to dependency isolation, compatibility, reproducibility, efficient sharing of specialized hardware, and deployment across heterogeneous environments. In this paper, we present CSAR (Containerized System Architecture for Robotics), a container-centric architectural framework designed specifically for robotics teams and the edge-cloud continuum. CSAR combines LXC/LXD-based system containerization, ROS 2/DDS-based communication, and a three-layer edge infrastructure to organize computation into hardware-affine, persistent execution environments that remain decoupled from the volatility of experimental workloads. Through its Infrastructure Core, Platform and Multi-User Orchestration, and Compute and Acceleration layers, CSAR provides strong isolation, controlled resource sharing, and topology-aware networking for distributed robotic applications. To demonstrate its validity, we describe a real deployment of CSAR in an academic robotics laboratory and evaluate it through representative use cases involving edge-offloaded 3D SLAM and GPU-accelerated semantic mapping. The results indicate that CSAR simplifies software integration, improves the utilization of shared computational resources, and facilitates safe prototyping, as well as reproducible and collaborative experimentation in robotics teams. The implementation described in this paper, including deployment templates, configuration files, and documentation, is available at https://github.com/goyoambrosio/CSAR.
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