Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention

Salvador López-Barajas (), Pedro J. Sanz, Raúl Marín-Prades, Juan Echagüe and Sebastian Realpe
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Salvador López-Barajas: Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, Spain
Pedro J. Sanz: Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, Spain
Raúl Marín-Prades: Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, Spain
Juan Echagüe: Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, Spain
Sebastian Realpe: Computer Vision and Robotics Research Institute (VICOROB), Universitat de Girona, 17003 Girona, Spain

Future Internet, 2025, vol. 17, issue 1, 1-21

Abstract: 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.

Keywords: underwater communications; visual light communication; underwater intervention systems; human–robot interaction; human in the loop (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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