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Whispers and Watts: An Empirical Study of the Energy Consumption of ROS 2 Topic-Based Communication

Authors Michel Albonico , Nuno Macedo

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LIPIcs.ESEM.2026.4.pdf
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Subject Classification

ACM Subject Classification
  • Software and its engineering → Software design tradeoffs
Keywords
  • ROS 2
  • Energy Consumption
  • Topic Communication
  • GitHub Mining

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Abstract

Background. The Robot Operating System (ROS) is a framework widely adopted for developing robotics applications. Its topic-based communication model allows distributed components to exchange data via message-passing, where suboptimal messaging configuration can lead to performance degradation and resource inefficiency.

Aims. This paper aims to investigate the energy consumption for ROS 2 topic-based communication, focusing on messaging configurations commonly used in real-world robotics projects.

Method. We systematically mine 112 active ROS 2 GitHub repositories to identify prevalent message types, frequencies, sizes, and number of subscribers. These insights guide the design of a controlled experiment in which we measure the power consumption of ROS 2 nodes across multiple configurations. 

Results. Analysis reveals that message type and interval significantly affect subscriber-side energy usage, with complex messages also affecting the publisher side. This evidence helps guide the design of energy-aware robotics software based on publish/subscribe communication.

Conclusions. ROS 2 topic-based communication design decisions, especially message type and publishing interval, have a substantial impact on energy consumption.

Cite As Get BibTex

Michel Albonico and Nuno Macedo. Whispers and Watts: An Empirical Study of the Energy Consumption of ROS 2 Topic-Based Communication. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ESEM.2026.4

Author Details

Michel Albonico
  • Centre for Software Technology, University of Southern Denmark (SDU), Vejle, Denmark
  • Federal University of Technology, Paraná (UTFPR), Francisco Beltrão, Brazil
Nuno Macedo
  • Departamento de Informática, Universidade do Minho, Braga, Portugal

Funding

Research launched under INESC TEC International Visiting Researcher Programme - 2023 Edition.

Acknowledgements

In this document, we only used AI (ChatGPT) to improve parts of the writing, especially relating to grammar corrections. All the AI changes were supervised by the authors.

Supplementary Materials

References

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