Tuning ROS 2 for Energy-Efficient Navigation: Empirical Insights from Costmap 2D Configurations
Michel Albonico, Andreas Wortmann, Ivano Malavolta
AI summary
Problem
Dynamic robotic environments often lead to suboptimal ROS 2 software configurations that degrade performance and increase energy consumption, yet empirical guidance for tuning navigation parameters remains scarce.
Approach
We performed a controlled simulation experiment across two warehouse layouts, testing 20 representative Costmap 2D configurations to measure their impact on energy usage, power demand, resource load, and navigation performance.
Key results
- Moderate resolution and low update frequencies consistently lowered energy consumption
- Reduced reliance on inflation and unknown-space layers improved navigation efficiency
- Excessive inflation and rough resolutions caused navigation failures and increased energy use
- Optimized configurations reduced overall energy by shortening navigation time despite higher instantaneous power
Why it matters
Offers actionable empirical guidelines for robotics developers and researchers to configure ROS 2 navigation stacks for sustainable, energy-efficient autonomous operations.
Abstract
Robots are increasingly used in diverse application areas, where autonomous navigation plays a central role. As these systems become more widespread, improving their energy efficiency is critical to extending operational time and reducing environmental impact. The Robot Operating System (ROS) is a widely adopted middleware for robotics, offering a rich set of configurable packages. However, this flexibility can result in suboptimal software configurations in dynamic environments, negatively affecting both performance and energy consumption. This paper investigates the impact of ROS 2 package re- configurations on the energy efficiency of mobile robot navi- gation. We conduct a controlled experiment in two warehouse- like scenarios (small and large) with varying obstacle layouts and Costmap 2D configurations (essential to the Nav2 stack). Through repeated trials, we measure energy usage, power profile, CPU load, memory consumption, and navigation performance. Results show that configurations must be carefully chosen for the specific robotic environment, and we were able to identify critical settings that lead to good and poor performance and energy consumption.