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ROS 2 has tools for measuring performance, but it does not provide one latency figure or middleware choice that guarantees real-time results for every robot. Set deadlines and jitter limits for your application, measure the complete system under representative conditions, and report the software and hardware configuration alongside the results.
What does real-time performance mean for your robot?
Start with the behavior the robot must meet, not a benchmark number borrowed from another system. For each performance-sensitive data or control path, define:
- Message rate and path: which messages travel between which components, and how often they are expected.
- End-to-end deadline: how long the system may take from the event or input that matters to the output that matters.
- Acceptable jitter: how much variation in timing the application can tolerate.
- Loss behavior: whether a dropped or late message is acceptable, and what the application should do if it happens.
- Missed-deadline condition: the precise event you will count as a failure.
These are application requirements, not universal ROS 2 guarantees. A system’s average timing alone cannot establish that it meets a deadline: examine worst observed values and variation as well as the average, and check whether the application’s loss and deadline requirements were met.
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Use Topic Statistics as one measurement signal
The ROS 2 Kilted documentation’s Topic Statistics tutorial describes subscription statistics for message_age and message_period. Its summaries include average, minimum, maximum, standard deviation, and sample count. These metrics can help characterize a system and diagnose timing issues.
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Read the measures together. The average describes typical behavior in the observed sample; maximum and standard deviation can reveal timing excursions or variation that an average conceals. The sample count helps show how much data underlies the summary. Topic Statistics are a useful signal, not by themselves proof that an entire robot’s end-to-end path meets its deadline.
Run your own representative test
The values shown in the tutorial are output from its example run, not a baseline or performance promise for other machines. Measure your own workload with the message sizes, CPU load, network conditions, and deployment topology your robot will encounter. Include enough run time and samples to capture the conditions relevant to your application, and distinguish a controlled test from a production deployment.
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What should a ROS 2 performance report record?
A latency result is meaningful only with the configuration that produced it. For a reproducible comparison, record:
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- ROS 2 distribution and version, and the selected RMW implementation.
- Host and target hardware, operating system, and kernel.
- Executor and callback configuration, plus the QoS profile.
- Deployment topology, including whether communication is within one host or across a network.
- Message types and sizes, tested rate, and relevant workload conditions.
- Test duration, measurement method, and the observed message-age and message-period summaries.
- CPU and memory observations, and whether deadline and loss requirements were met.
This is a practical reporting checklist drawn from the factors that can vary across ROS 2 deployments and the available measurement tools; it is not a ROS 2 mandated report format.
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Which ROS 2 middleware should you benchmark?
ROS 2 supports multiple middleware implementations through RMW. The ROS 2 middleware overview describes Fast DDS as the default packaged implementation. It characterizes Cyclone DDS as lighter and optimized for deterministic real-time communication, and Zenoh, beginning with Kilted, as designed for IoT and edge situations with an emphasis on high throughput, low latency, and interoperability across heterogeneous environments. These are descriptions in the documentation, not a guarantee that one option will perform best for a particular robot.
The same overview lists licensing, platform availability, resource utilization, and computation footprint as selection factors. Treat those alongside your workload’s measured latency and throughput, QoS needs, and deployment topology.
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- Suitable for tech enthusiasts, makers, or beginners in programming, it is your ideal choice for exploring the world of intelligent technology.
- Equipped with the high-performance Jetson Orin series computer to meet the challenges of complex strategies and functions, and inspire your creativity. Adopts dual-controller design, combines the high-level AI functions of the host controller with the high-frequency basic operations of the sub controller, making every operation accurate and smooth.
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| Implementation | What the ROS 2 overview says | What to verify for your deployment |
|---|---|---|
| Fast DDS | Default packaged implementation, as described by the overview. | Licensing, platform availability, resource use, computation footprint, and measured results for your workload. |
| Cyclone DDS | Described as lighter and optimized for deterministic real-time communication. | Whether it meets your application’s deadlines, jitter tolerance, resource limits, and platform requirements. |
| Zenoh | Supported beginning with Kilted; described for IoT and edge situations, emphasizing high throughput, low latency, and interoperability across heterogeneous environments. | Whether its deployment characteristics and measured results fit your system and target ROS 2 distribution. |
| RTI Connext | Listed as a supported RMW implementation; further characteristics not stated in the overview. | Licensing, platform availability, resource use, computation footprint, and measured results for your workload. |
| GurumDDS | Listed as a supported RMW implementation; further characteristics not stated in the overview. | Licensing, platform availability, resource use, computation footprint, and measured results for your workload. |
Middleware-to-middleware communication may work in many cases, but cross-vendor DDS interoperability is not guaranteed in every combination. For a distributed deployment, use a consistent ROS version and RMW unless you have tested the exact combination you intend to run. Treat interoperability as a separate test from latency or throughput.
How can you investigate jitter and performance problems?
Change one layer or variable at a time, repeat the same workload, and compare the same measurements. That makes it easier to tell whether an observed change came from middleware, system load, network conditions, or application configuration rather than from a different test.
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- If timing summaries vary, compare average, maximum, standard deviation, and sample count; do not rely on the average alone.
- If a deadline is missed, use timestamps and measurements across the relevant path to narrow down where time is being spent.
- If resource pressure may be involved, collect CPU and memory observations alongside timing data.
- If results differ across machines or deployments, check that the workload, ROS version, RMW, kernel, QoS, executor setup, and topology are comparable before attributing the difference to a single factor.
The Jazzy performance_test package documentation lists utilities for CPU-usage tracking, memory checking, real-time enabling, and timestamps. The rcl quality declaration says performance analysis is conducted per release rather than per change and points to system-level benchmarks. These resources can inform an investigation, but they do not replace measuring the application and deployment you need to validate.
The Kilted documentation index also points to resources on real-time programming, tracing, DDS tuning, executors, QoS, and building a real-time Linux kernel. Consult the documentation for the exact ROS distribution and platform you use before applying configuration guidance; availability and details can vary by release.
How should you present a performance result?
State the result as an observation about one tested configuration, not as a general ROS 2 capability. Include the workload and deployment details that affect interpretation, identify the measurement method, and say whether the application’s actual deadline and loss requirements were satisfied. No generalizable numeric real-time benchmark figure is established by the cited documentation for ROS 2 as a whole.
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