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. 2025 Apr 2:12:1548250.
doi: 10.3389/frobt.2025.1548250. eCollection 2025.

Energy efficiency in ROS communication: a comparison across programming languages and workloads

Michel Albonico  1 , Manuela Bechara Cannizza  1 , Andreas Wortmann  2
Affiliations

Energy efficiency in ROS communication: a comparison across programming languages and workloads

Michel Albonico et al. Front Robot AI. .

Abstract

Introduction: The Robot Operating System (ROS) is a widely used framework for robotic software development, providing robust client libraries for both C++ and Python. These languages, with their differing levels of abstraction, exhibit distinct resource usage patterns, including power and energy consumption-an increasingly critical quality metric in robotics.

Methods: In this study, we evaluate the energy efficiency of ROS two nodes implemented in C++ and Python, focusing on the primary ROS communication paradigms: topics, services, and actions. Through a series of empirical experiments, with programming language, message interval, and number of clients as independent variables, we analyze the impact on energy efficiency across implementations of the three paradigms.

Results: Our data analysis demonstrates that Python consistently demands more computational resources, leading to higher power consumption compared to C++. Furthermore, we find that message frequency is a highly influential factor, while the number of clients has a more variable and less significant effect on resource usage, despite revealing unexpected architectural behaviors of underlying programming and communication layers.

Keywords: ROS; ROS communication; energy efficiency; programming language; robotic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
UML sequence diagram for publisher/subscriber communication.
FIGURE 2
FIGURE 2
UML sequence diagram for service communication.
FIGURE 3
FIGURE 3
UML sequence diagram for action communication.
FIGURE 4
FIGURE 4
Underlying layers of a ROS node programming.
FIGURE 5
FIGURE 5
Adapted UML deployment diagram of experiment instrumentation.
FIGURE 6
FIGURE 6
Power distribution for the publisher node across 20 executions, varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 7
FIGURE 7
Power distribution for the publisher node across 20 executions, varying the number of clients at 0.2-s message interval. (A) Overall distribution at 0.2 s interval, (B) Distribution at 0.2 s interval by number of clients.
FIGURE 8
FIGURE 8
Power consumption distribution for one subscriber node across 20 executions, while varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 9
FIGURE 9
Power consumption distribution for the service server node across 20 executions, varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 10
FIGURE 10
Power consumption distribution for one service client node across 20 executions, while varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 11
FIGURE 11
Power consumption distribution for the action server node across 20 executions, varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 12
FIGURE 12
Power consumption distribution for one action client node across 20 executions, while varying the number of clients and message interval. (A) Message interval: 0.05 s, (B) Message interval: 0.1 s, (C) Message interval: 0.2 s, (D) Message interval: 0.5 s, (E) Message interval: 1.0 s.
FIGURE 13
FIGURE 13
Power consumption distribution over all the combinations of independent variables for publisher/server nodes across the 20 executions.
FIGURE 14
FIGURE 14
Power consumption distribution over all the combinations of independent variables for subscriber/client nodes across the 20 executions.
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