ROSCell: A ROS2-Based Framework for Automated Formation and Orchestration of Multi-Robot Systems

2026-03-24

AI Summary

This work proposes a lightweight and scalable multi-robot orchestration framework based on ROS 2 to address the challenges of flexible configuration, rapid reconfiguration, and efficient coordination in high-mix, low-volume manufacturing environments. The framework encapsulates robot functionalities as deployable skills and leverages Compute Continuum principles to automatically construct isolated execution units, dynamically instantiate skill deployments, and enable resource-aware communication coordination. Experimental results demonstrate that the system significantly reduces CPU, memory, and network overhead in idle states, outperforming K3s-based solutions in energy efficiency and overall performance, thereby making it well-suited for large-scale edge deployment scenarios.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern manufacturing under High-Mix-Low-Volume requirements increasingly relies on flexible and adaptive matrix production systems, which depend on interconnected heterogeneous devices and rapid task reconfiguration. To address these needs, we present ROSCell, a ROS2-based framework that enables the flexible formation and management of a computing continuum across various devices. ROSCell allows users to package existing robotic software as deployable skills and, with simple requests, assemble isolated cells, automatically deploy skill instances, and coordinate their communication to meet task objectives. It provides a scalable and low-overhead foundation for adaptive multi-robot computing in dynamic production environments. Experimental results show that, in the idle state, ROSCell substantially reduces CPU, memory, and network overhead compared to K3s-based solutions on edge devices, highlighting its energy efficiency and cost-effectiveness for large-scale deployment in production settings. The source code, examples, and documentation will be provided on Github.

Problem

• multi-robot systems
• High-Mix-Low-Volume manufacturing
• adaptive production
• heterogeneous devices
• task reconfiguration

Innovation

• ROSCell
• ROS2
• multi-robot systems
• edge computing
• adaptive manufacturing