Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention
In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human–Robot Interface (HRI) with...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/10 |
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| _version_ | 1832588401901043712 |
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| author | Salvador López-Barajas Pedro J. Sanz Raúl Marín-Prades Juan Echagüe Sebastian Realpe |
| author_facet | Salvador López-Barajas Pedro J. Sanz Raúl Marín-Prades Juan Echagüe Sebastian Realpe |
| author_sort | Salvador López-Barajas |
| collection | DOAJ |
| description | In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human–Robot Interface (HRI) with a network congestion control algorithm at the application level for conservative transmission of images using the Robot Operating System (ROS) framework. The system was designed to avoid network congestion by adjusting the image compression parameters and the transmission rate depending on the real-time network conditions. To evaluate its performance, the algorithm was tested in two wireless underwater use cases: pipe inspection and an intervention task. An Autonomous Underwater Vehicle for Intervention (I-AUV) equipped with a Visual Light Communication (VLC) modem was used. Characterization of the VLC network was performed while the robot performed trajectories in the tank. The results demonstrate that our approach allows an operator to perform wireless missions where teleoperation requires images and the network conditions are variable. This solution provides a robust framework for image transmission and network control in the application layer, which allows for integration with any ROS-based system. |
| format | Article |
| id | doaj-art-2ddbbdfdfce24807ba1a1962ec1b037d |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-2ddbbdfdfce24807ba1a1962ec1b037d2025-01-24T13:33:33ZengMDPI AGFuture Internet1999-59032025-01-011711010.3390/fi17010010Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for InterventionSalvador López-Barajas0Pedro J. Sanz1Raúl Marín-Prades2Juan Echagüe3Sebastian Realpe4Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, SpainInteractive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, SpainInteractive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, SpainInteractive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, SpainComputer Vision and Robotics Research Institute (VICOROB), Universitat de Girona, 17003 Girona, SpainIn this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human–Robot Interface (HRI) with a network congestion control algorithm at the application level for conservative transmission of images using the Robot Operating System (ROS) framework. The system was designed to avoid network congestion by adjusting the image compression parameters and the transmission rate depending on the real-time network conditions. To evaluate its performance, the algorithm was tested in two wireless underwater use cases: pipe inspection and an intervention task. An Autonomous Underwater Vehicle for Intervention (I-AUV) equipped with a Visual Light Communication (VLC) modem was used. Characterization of the VLC network was performed while the robot performed trajectories in the tank. The results demonstrate that our approach allows an operator to perform wireless missions where teleoperation requires images and the network conditions are variable. This solution provides a robust framework for image transmission and network control in the application layer, which allows for integration with any ROS-based system.https://www.mdpi.com/1999-5903/17/1/10underwater communicationsvisual light communicationunderwater intervention systemshuman–robot interactionhuman in the loop |
| spellingShingle | Salvador López-Barajas Pedro J. Sanz Raúl Marín-Prades Juan Echagüe Sebastian Realpe Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention Future Internet underwater communications visual light communication underwater intervention systems human–robot interaction human in the loop |
| title | Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention |
| title_full | Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention |
| title_fullStr | Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention |
| title_full_unstemmed | Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention |
| title_short | Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention |
| title_sort | network congestion control algorithm for image transmission hri and visual light communications of an autonomous underwater vehicle for intervention |
| topic | underwater communications visual light communication underwater intervention systems human–robot interaction human in the loop |
| url | https://www.mdpi.com/1999-5903/17/1/10 |
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