Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness

TL;DR

This paper reviews emerging architectures and technologies for distributed robotic systems using ROS 2, focusing on edge-cloud deployment, containerization, simulation, and continuous deployment, highlighting current readiness and future challenges.

Contribution

It provides a comprehensive overview of recent innovations in ROS 2-based distributed robotic architectures, including containerization, cloud integration, and advanced simulation techniques.

Findings

New architectures enable seamless edge-cloud robotic deployment.

Integration of Kubernetes and ROS 2 enhances system scalability.

Cloud simulations facilitate large-scale reinforcement learning.

Abstract

Robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the \textit{de facto} robotics middleware standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies that enable containerized robotic applications to seamlessly run at the edge or in the cloud. We also overview systems that include solutions from extension to ROS\,2 tooling to the integration of Kubernetes and ROS\,2. Another important trend is robot learning, and how new simulators and cloud simulations are enabling, e.g., large-scale reinforcement learning or distributed federated learning solutions. This has also enabled deeper integration of continuous interaction and continuous deployment (CI/CD) pipelines for robotic systems development, going beyond standard software unit tests with simulated tests to build and validate code automatically. We discuss the current technology readiness and list the potential new application scenarios that are becoming available. Finally, we discuss the current challenges in distributed robotic systems and list open research questions in the field.