Offloading Robot Control with 5G

Sossalla, Peter; Rischke, Justus; Nguyen, Giang T.; Fitzek, Frank H. P.

doi:10.1109/ccnc49033.2022.9700709

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In 6G, this trend continues, with more sophisticated Edge Computing schemes such as federated learning [175]; and the addition of xApps allows a cross-layer integration between end-to-end services and the CU/DU. While location already benefits from Edge Computing in 5G [183], [184], the integration with CU/DU can further enhance location accuracy, for instance, by connecting it with external data services that allow context awareness (e.g., by integrating geographical Application Programming Interfaces -APIs-in the estimation of distances with physical magnitudes). This can be done without significantly increasing location acquisition latency.…”

Section: ) Edge Computingmentioning

confidence: 99%

Designing a 6G Testbed for Location: Use Cases, Challenges, Enablers and Requirements

Khatib¹,

Álvarez‐Merino²,

Luo-Chen³

et al. 2023

IEEE Access

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Location will have a central role in Research and Development (R&D) towards 6G networks, both as a service offered by the network (improving the current offering of 5G) and as an input to increasingly location-aware services and network functions. To integrate location into 6G standards, it will be very important to design validation systems such as testbeds, even when the actual technology is not yet commercially available. This paper performs a review of the use cases and their requirements, enabling technologies in 6G, and challenges; and proposes a flexible testbed architecture for performing network location related R&D. This architecture will allow to deploy an evolving infrastructure which will allow early validation of 6G technologies.

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Section: ) Edge Computingmentioning

confidence: 99%

Designing a 6G Testbed for Location: Use Cases, Challenges, Enablers and Requirements

Khatib¹,

Álvarez‐Merino²,

Luo-Chen³

et al. 2023

IEEE Access

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“…Particularly the trend of edge cloud computing [16]- [21] to perform computation as close as possible to the end user, i.e., at the edge of the network, has enabled the offloading of latency-critical applications, such as SLAM. With the upcoming usage of private 5G networks [22], virtualized robot control [23] with edge computing has gained traction. The study [24] demonstrated that the increased computing power supplied by edge computing speeds up the SLAM processing and reduces the load on the mobile end device.…”

Section: A Motivation For Slam Network Offloadingmentioning

confidence: 99%

DynNetSLAM: Dynamic Visual SLAM Network Offloading

et al. 2022

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Existing Visual Simultaneous Localization And Mapping (vSLAM) approaches that offload the complex self-localization computations from mobile robots over a wireless network to edge computing are limited to static offloading, i.e., the offloaded computation tasks are offloaded permanently. However, wireless networks are inherently dynamic and may excessively delay the transmissions between a mobile device and the edge during periods of poor wireless network quality, e.g., from fading or temporary obstructions. We propose and evaluate Dynamic Visual SLAM Network Offloading (DynNetSLAM) to dynamically adapt the vSLAM computation offloading according to the measured wireless network latency. As groundwork towards developing DynNetSLAM, we first enhance the existing state-of-the-art vSLAM approaches through judicious parameter settings and parallel map updates to enable the tracking of common fast vSLAM data sets. We introduce an offloading latency threshold along with a safe zone and a hysteresis around the threshold to control the dynamic offloading. Our extensive evaluations with public vSLAM data sets indicate that DynNetSLAM with the hysteresis substantially reduces the probability of track loss events compared to the state-of-the-art ORB-SLAM2 approach for processing statically on the mobile device and the enhanced static Edge SLAM. Also, DynNetSLAM nearly attains the low absolute position error and only slightly increases the CPU utilization compared to the enhanced static Edge SLAM.

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