CSAR: Containerized System Architecture for Robotics
2026-06-29 • Robotics
Robotics
AI summaryⓘ
The authors present CSAR, a system designed to help teams working on robots manage complex software and hardware setups. CSAR uses containers to keep different parts of the software isolated and organized, making it easier to share resources and run experiments safely. They tested CSAR in a real robotics lab with tasks like 3D mapping, showing it helps teams work together better and use computing power more efficiently. The system and its documentation are publicly available for others to use.
RoboticsContainerizationROS 2Edge computingCloud computingLXC/LXD containers3D SLAMSemantic mappingDistributed systemsResource sharing
Authors
Ambrosio-Cestero, Gregorio, Galindo Andrades, Cipriano, Gonzalez-Jimenez, Javier, Ruiz-Sarmiento, Jose-Raul
Abstract
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.