Reconciling ROS 2 with Classical Real-Time Scheduling of Periodic Tasks
The Robot Operating System 2 (ROS 2) is a widely used middleware that provides software libraries and tools for developing robotic systems. In these systems, tasks are scheduled by ROS 2 executors. Since the scheduling behavior of the default ROS 2 executor is inherently different from classical real-time scheduling theory, dedicated analyses or alternative executors requiring substantial changes to ROS 2 have been developed. In 2023, the events executor was introduced into ROS 2. It features an events queue and allows the possibility to make scheduling decisions immediately after a job is completed. In this paper, we show that with minor modifications of the events executor, a large body of research results from classical real-time scheduling theory becomes directly applicable to ROS 2. This enables analytical bounds on the worst-case response time and the end-to-end latency, outperforming bounds for the default ROS 2 executor in many scenarios. Our solution is easy to integrate into existing ROS 2 systems since it requires only minor modifications of the events executor, which is natively included in ROS 2. The evaluation results show that our ROS 2 events executor with minor modifications can have significant improvement in terms of dropped jobs, worst-case response time, end-to-end latency, and performance compared to the default ROS 2 executor.
Authors
- Mario Günzel (ORCID: https://orcid.org/0000-0001-7575-7014)
- Kuan-Hsun Chen (ORCID: https://orcid.org/0000-0002-7110-921X)
- Harun Teper (ORCID: https://orcid.org/0000-0003-2873-9096)
- Christopher Gill (ORCID: https://orcid.org/0000-0003-0366-8586)
- Oren Bell
Institutions
- TU Dortmund University (DE)
- Washington University in St. Louis (US)
Publication Details
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1109/rtas65571.2025.00027
- Citations
- 2
- Primary Topic
- Scheduling and Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Science Foundation