758 research outputs found
I-AUV Docking and Panel Intervention at Sea
The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea.This work was supported by the Spanish project DPI2014-57746-C3 (MERBOTS Project) and
by Generalitat Valenciana under Grant GVA-PROMETEO/2016/066. The University of Girona wants to thank the
SARTI group for their collaboration with the TRITON project
I-AUV Docking and Panel Intervention at Sea
The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea.This work was supported by the Spanish project DPI2014-57746-C3 (MERBOTS Project) and
by Generalitat Valenciana under Grant GVA-PROMETEO/2016/066. The University of Girona wants to thank the
SARTI group for their collaboration with the TRITON project
Simultaneous Trajectory Estimation and Mapping for Autonomous Underwater Proximity Operations
Due to the challenges regarding the limits of their endurance and autonomous
capabilities, underwater docking for autonomous underwater vehicles (AUVs) has
become a topic of interest for many academic and commercial applications.
Herein, we take on the problem of state estimation during an autonomous
underwater docking mission. Docking operations typically involve only two
actors, a chaser and a target. We leverage the similarities to proximity
operations (prox-ops) from spacecraft robotic missions to frame the diverse
docking scenarios with a set of phases the chaser undergoes on the way to its
target. We use factor graphs to generalize the underlying estimation problem
for arbitrary underwater prox-ops. To showcase our framework, we use this
factor graph approach to model an underwater homing scenario with an active
target as a Simultaneous Localization and Mapping problem. Using basic AUV
navigation sensors, relative Ultra-short Baseline measurements, and the
assumption of constant dynamics for the target, we derive factors that
constrain the chaser's state and the position and trajectory of the target. We
detail our front- and back-end software implementation using open-source
software and libraries, and verify its performance with both simulated and
field experiments. Obtained results show an overall increase in performance
against the unprocessed measurements, regardless of the presence of an
adversarial target whose dynamics void the modeled assumptions. However,
challenges with unmodeled noise parameters and stringent target motion
assumptions shed light on limitations that must be addressed to enhance the
accuracy and consistency of the proposed approach.Comment: 19 pages, 14 figures, submitted to the IEEE Journal of Oceanic
Engineerin
Robust Continuous System Integration for Critical Deep-Sea Robot Operations Using Knowledge-Enabled Simulation in the Loop
Deep-sea robot operations demand a high level of safety, efficiency and
reliability. As a consequence, measures within the development stage have to be
implemented to extensively evaluate and benchmark system components ranging
from data acquisition, perception and localization to control. We present an
approach based on high-fidelity simulation that embeds spatial and
environmental conditions from recorded real-world data. This simulation in the
loop (SIL) methodology allows for mitigating the discrepancy between simulation
and real-world conditions, e.g. regarding sensor noise. As a result, this work
provides a platform to thoroughly investigate and benchmark behaviors of system
components concurrently under real and simulated conditions. The conducted
evaluation shows the benefit of the proposed work in tasks related to
perception and self-localization under changing spatial and environmental
conditions.Comment: published on IROS 201
Robust Vision-based Underwater Target Identification & Homing Using Self-Similar Landmarks
International audienceNext generation Autonomous Underwater Vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localisation and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods, however, reliable target segmentation often plagues these systems. This paper addresses robust vision-based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on Self-Similar Landmarks (SSL) that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision-based docking with the target. Experimental results show that system performs exceptionally on limited processing power and demonstrates how the combined vision and controller systems enables robust target identification and docking in a variety of operating conditions
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