RSLCPP - Deterministic Simulations Using ROS 2

2026-01-11

arXiv.org

AI Summary

This work addresses the non-reproducibility of ROS 2 simulations across different hardware platforms, a critical issue stemming from its asynchronous multi-process architecture that undermines scientific evaluation and continuous integration. We propose a non-intrusive approach that enforces deterministic execution by unifying the callback scheduling order of ROS 2 C++ nodes without requiring any modifications to existing codebases. To our knowledge, this is the first method to achieve fully reproducible simulation for ROS 2 C++ nodes. We introduce the ROS Simulation Library for C++ (RSLCPP), which provides precise control over execution timing and communication sequencing. Experimental validation on both synthetic benchmarks and real-world robotic systems demonstrates that our approach yields bit-for-bit identical simulation results across diverse CPU architectures. The implementation is publicly available as open-source software.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Simulation is crucial in real-world robotics, offering safe, scalable, and efficient environments for developing applications, ranging from humanoid robots to autonomous vehicles and drones. While the Robot Operating System (ROS) has been widely adopted as the backbone of these robotic applications in both academia and industry, its asynchronous, multiprocess design complicates reproducibility, especially across varying hardware platforms. Deterministic callback execution cannot be guaranteed when computation times and communication delays vary. This lack of reproducibility complicates scientific benchmarking and continuous integration, where consistent results are essential. To address this, we present a methodology to create deterministic simulations using ROS 2 nodes. Our ROS Simulation Library for C++ (RSLCPP) implements this approach, enabling existing nodes to be combined into a simulation routine that yields reproducible results without requiring any code changes. We demonstrate that our approach yields identical results across various CPUs and architectures when testing both a synthetic benchmark and a real-world robotics system. RSLCPP is open-sourced at https://github.com/TUMFTM/rslcpp.

Problem

• deterministic simulation
• ROS 2
• reproducibility
• callback execution
• robotics

Innovation

• deterministic simulation
• ROS 2
• reproducibility
• RSLCPP
• callback execution