At Open Source Summit Japan in July 2019, Pintu Kumar presented a Raspberry Pi 3 prototype that used Linux 4.9 and Xenomai 3 to give an ultrasonic-sensor thread higher scheduling priority for a hypothetical sudden-obstacle stop. It was an illustrative model—not a deployed robot or a safety-validated system—and its reported timing figures are presentation results, not independently reproduced benchmarks.
What the 2019 model demonstrated
Kumar’s presentation, “Xenomai Based Real Time Model Without Using RTOS”, described a way to add real-time behavior to a Linux-based system rather than using a separate conventional RTOS. Its example hardware was a Raspberry Pi 3 single-board computer running a kernel based on the rpi-4.9.y branch, with Xenomai 3.
The example was intentionally narrow: an ultrasonic sensor checks for a nearby obstacle and initiates a motor-stop action if the measured distance reaches an example threshold. Kumar’s slides call this use case hypothetical; they do not establish a physical deployment, a safety case, or suitability for controlling a real vehicle or robot.
How the system was structured
The presentation’s model consists of C-language components for Bluetooth serial control, GPIO, LEDs, motor control, switches, and an ultrasonic sensor. The example ran as a systemd service. Rather than assigning real-time treatment to every component, it focused elevated priority on the part considered most time-critical: the ultrasonic-sensor thread.
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“Not everything needs to be real time in a system.”
—Pintu Kumar, OSS Japan 2019 presentation
The accompanying guidance is to identify the critical part of the system, then apply high priority selectively. That design principle is more general than the prototype’s exact hardware, but the slides do not establish that the implementation can be transferred unchanged to other boards or kernels.
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The 2019 kernel and Xenomai setup
For its Raspberry Pi 3 configuration, the deck describes applying an I-pipe patch to the Linux kernel and then applying Xenomai kernel patches, followed by kernel configuration and build. It also describes building and installing Xenomai user space, including the Cobalt layer, POSIX support, and the Alchemy skin.
- Start from the Raspberry Pi kernel branch identified in the slides:
rpi-4.9.y. - Apply the I-pipe patch, followed by the Xenomai kernel patches for that setup.
- Configure and build the patched kernel.
- Build and install the Xenomai user-space components used by the application.
- Rebuild application components if required, then configure the example service to run under systemd.
These are historical details of the 2019 project, not current installation instructions. The presentation warns that Xenomai support was limited to certain SoCs and kernel versions and that applications might need rebuilding. It does not establish compatibility of this patch combination with current Raspberry Pi boards, kernels, or Xenomai releases. Kernel-patch maintenance, vendor-kernel porting, debugging, and system tuning were also identified as engineering costs.
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How the obstacle-response example worked
In the slides’ illustrative configuration, the ultrasonic thread uses SCHED_FIFO at priority 99, checks distance every 100 milliseconds, and triggers an example motor-stop action at or below 50 centimeters. The 100 ms interval and 50 cm threshold are parameters shown for the presentation’s hypothetical scenario; they are not validated operating limits for any particular sensor, motor, or robot.
The point of assigning high priority to that thread is to prioritize one time-sensitive task, not to make every operation in the system real time. A high scheduling priority alone does not prove that a complete system will meet a deadline: behavior also depends on the kernel, hardware, workload, device interactions, and implementation.
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What the presentation reported—and what it does not prove
For the plotted ultrasonic-task response, Kumar’s presentation reports a minimum of 104.095 ms, a maximum of 104.515 ms, and an average of 104.175 ms across “1000+” samples. These are figures printed in the 2019 slides. They are not an independently reproduced benchmark, and the presentation details available here do not provide enough conditions to treat them as a transferable performance guarantee.
The slides also label experiments comparing normal Linux and Xenomai POSIX behavior for 100-microsecond tasks, with and without load, a native-API comparison, 10-millisecond task comparisons, and cyclictest runs under moving and no-movement conditions. Some extracted moving-condition cyclictest maximums are 3,630, 1,013, 203,509, and 1,130 as printed in the slides. The available plot context is insufficient to map each value confidently to a particular configuration, so they should not be read as a ranked comparison.
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The presentation includes no organization-authored industry survey or population statistic relevant to this prototype. Its numbers are task parameters and reported experiment outputs, not broad statistics about Xenomai or real-time Linux performance.
What to consider when evaluating a similar design
The 2019 example is useful as a design illustration, but a present-day project needs to evaluate its own target and workload. In particular:
- Hardware and kernel support: verify that the exact SoC, board, vendor kernel, and kernel version are supported by the real-time approach being considered.
- Integration and upkeep: account for patching and maintaining the kernel, especially when starting from a vendor kernel.
- Application work: determine whether existing components need rebuilding or API changes, and which Xenomai interfaces are appropriate.
- Measured behavior: test latency under the actual system load and conditions that matter to the application; do not infer current performance from a 2019 demonstration.
- Operations: include the skills and time needed for debugging and system tuning.
Kumar’s slides identify both PREEMPT_RT and Xenomai as Linux real-time options that involve kernel changes, but do not provide a comprehensive comparison or name a universal winner. A meaningful choice depends on the target SoC and kernel, integration and maintenance demands, application porting, latency under the intended workload, and operational debugging burden.
Limits of the available build details
The slides do not establish the Raspberry Pi 3 board revision, ultrasonic sensor model, full bill of materials, or reproducibility conditions sufficient for an independent benchmark. They also do not establish the present-day support status of the specific 2019 patch combination. Accordingly, the hardware description should remain at the level of Raspberry Pi 3 and an ultrasonic sensor, and the results should remain attributed to the presentation.
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