# Tracking data of a Remotely Operated Vehicle and its tether using a motion capture system and a tension sensor

## TL;DR

This paper introduces a dataset tracking an underwater ROV and its tether using motion capture and tension sensors for improving control systems.

## Contribution

The novelty lies in combining motion capture and tension data to study underwater tether behavior for machine learning and control applications.

## Key findings

- The dataset captures ROV and tether motion using a motion capture system.
- Tension sensor data reveals forces exerted on the tether near the drum.
- The dataset supports modeling and control research for underwater tether systems.

## Abstract

In this paper, we present a dataset related to the behaviour of underwater umbilicals of a Remotely Operated Vehicle (ROV), specifically the BlueROV2. The data were collected from three different sources: a Motion Capture System (mo-cap system), the onboard sensors of the BlueROV2, and a tension sensor. The mo-cap system tracks the motion of the ROV and its tether, while the tension sensor measures the force exerted by the cable tension on the surface side, near the tether drum. The dataset were acquired for research purposes, specifically for studying underwater cables in the context of modeling, estimation, and control using machine learning and data-driven control methods. They can serve as a benchmark for developing and validating underwater tether models, enabling researchers to develop control methods that consider tether drag or entanglement, improving navigation accuracy of ROVs, or developing automated tether management systems. This paper describes the experimental setup, data acquisition, and post-processing procedures. Furthermore, it provides illustrative plots to highlight key features of the dataset.