We present NeRTA (Next Release Time Analysis), a technique to schedule dynamic software updates of the low-level control loops of mobile robots. Dynamic software updates enable software correction and evolution during system operation. In mobile robotics, they are crucial to resolve software defects without interrupting system operation or to enable on-the-fly extensions. Low-level control loops of mobile robots, however, are time sensitive and run on resource-constrained hardware with no operating system support. To minimize the impact of the update process, NeRTA safely schedules updates during times when the computing unit would otherwise be idle. It does so by utilizing information from the existing scheduling algorithm without impacting its operation. As such, NeRTA works orthogonal to the existing scheduler, retaining the existing platform-specific optimiza-tions and fine-tuning, and may simply operate as a plug-in component. Our experimental evaluation shows that NeRTA estimates are within 15% of the actual idle times in more than three-quarters of the cases. We also show that the processing overhead of NeRTA is essentially negligible.
NeRTA: Enabling Dynamic Software Updates in Mobile Robotics
Mottola L.;
2022-01-01
Abstract
We present NeRTA (Next Release Time Analysis), a technique to schedule dynamic software updates of the low-level control loops of mobile robots. Dynamic software updates enable software correction and evolution during system operation. In mobile robotics, they are crucial to resolve software defects without interrupting system operation or to enable on-the-fly extensions. Low-level control loops of mobile robots, however, are time sensitive and run on resource-constrained hardware with no operating system support. To minimize the impact of the update process, NeRTA safely schedules updates during times when the computing unit would otherwise be idle. It does so by utilizing information from the existing scheduling algorithm without impacting its operation. As such, NeRTA works orthogonal to the existing scheduler, retaining the existing platform-specific optimiza-tions and fine-tuning, and may simply operate as a plug-in component. Our experimental evaluation shows that NeRTA estimates are within 15% of the actual idle times in more than three-quarters of the cases. We also show that the processing overhead of NeRTA is essentially negligible.File | Dimensione | Formato | |
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