Research

18 pages, 3190 KiB

Open AccessArticle

Energy-Efficient Configuration and Control Allocation for a Dynamically Reconfigurable Underwater Robot

by Tho Dang, Lionel Lapierre, Rene Zapata and Benoit Ropars

Sensors 2023, 23(12), 5439; https://doi.org/10.3390/s23125439 - 08 Jun 2023

Viewed by 905

A dynamically reconfigurable underwater robot, which can vary its configuration during a mission, would be useful for confined environment exploration and docking because of its versatility. A mission can be performed by choosing among different configurations, and the energy cost may increase, owing [...] Read more.

A dynamically reconfigurable underwater robot, which can vary its configuration during a mission, would be useful for confined environment exploration and docking because of its versatility. A mission can be performed by choosing among different configurations, and the energy cost may increase, owing to the reconfigurability of the robot. Energy saving is the critical issue in long-range missions with underwater robots. Moreover, control allocation must be considered for a redundant system and input constraints. We propose an approach for an energy-efficient configuration and control allocation for a dynamically reconfigurable underwater robot that is built for karst exploration. The proposed method is based on sequential quadratic programming, which minimizes an energy-like criterion with respect to robotic constraints, i.e., mechanical limitations, actuator saturations, and a dead zone. The optimization problem is solved in each sampling instant. Two popular tasks for underwater robots, i.e., path-following and station-keeping (observation) problems, are simulated, and the simulation results show the efficiency of the method. Moreover, an experiment is carried out to highlight the results. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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26 pages, 17907 KiB

Open AccessArticle

Rotary 3D Magnetic Field Scanner for the Research and Minimization of the Magnetic Field of UUV

by Karol Jakub Listewnik and Kacper Aftewicz

Sensors 2023, 23(1), 345; https://doi.org/10.3390/s23010345 - 29 Dec 2022

Cited by 1 | Viewed by 1748

Abstract

Research on the value and nature of physical quantities allows for a detailed understanding of the conditions in the studied area, and the quality and precision of the final conclusions depend on the accuracy of the measurements. In order to increase the accuracy [...] Read more.

Research on the value and nature of physical quantities allows for a detailed understanding of the conditions in the studied area, and the quality and precision of the final conclusions depend on the accuracy of the measurements. In order to increase the accuracy of measurements, the measurement infrastructure and unmanned vehicles used during the observation should introduce the lowest possible disturbance–they should be minimized in terms of the magnetic field. This article presents a solution based on the infrastructure model and the development of a method using polynomial regression to study the magnetic field in three dimensions (3D-longitudinal X, transverse Y, and vertical Z components). The test stand consists of an Arduino Mega microcontroller, a rotary table driven and controlled by a stepper motor, a touch display whose task is to control the magnetic field measurement parameters and display 3D data, and proprietary software made in the Python programming language. The structural elements of the stand model were produced by an additive method using a 3D printer. The presented solution belongs to the group of modern technological solutions known as the technology of low object detection (stealth technology or low observable technology). Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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20 pages, 4172 KiB

Open AccessArticle

Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals

by Sang-Jae Yeo, Woen-Sug Choi, Suk-Yoon Hong and Jee-Hun Song

Sensors 2022, 22(18), 7073; https://doi.org/10.3390/s22187073 - 19 Sep 2022

Cited by 8 | Viewed by 1539

Abstract

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a [...] Read more.

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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30 pages, 36753 KiB

Open AccessArticle

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

by Eduardo Ochoa, Nuno Gracias, Klemen Istenič, Josep Bosch, Patryk Cieślak and Rafael García

Sensors 2022, 22(14), 5354; https://doi.org/10.3390/s22145354 - 18 Jul 2022

Cited by 4 | Viewed by 2513

Abstract

Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and [...] Read more.

Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), is often compromised. Common applications for obstacle avoidance in underwater environments are often conducted with acoustic sensors, which cannot be used reliably at very short distances, thus requiring a high level of attention from the operator to avoid damaging the robot. Therefore, developing capabilities such as advanced assisted mapping, spatial awareness and safety, and user immersion in confined environments is an important research area for human-operated underwater robotics. In this paper, we present a novel approach that provides an ROV with capabilities for navigation in complex environments. By leveraging the ability of omnidirectional multi-camera systems to provide a comprehensive view of the environment, we create a 360° real-time point cloud of nearby objects or structures within a visual SLAM framework. We also develop a strategy to assess the risk of obstacles in the vicinity. We show that the system can use the risk information to generate warnings that the robot can use to perform evasive maneuvers when approaching dangerous obstacles in real-world scenarios. This system is a first step towards a comprehensive pilot assistance system that will enable inexperienced pilots to operate vehicles in complex and cluttered environments. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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24 pages, 9471 KiB

Open AccessArticle

Sidescan Only Neural Bathymetry from Large-Scale Survey

by Yiping Xie, Nils Bore and John Folkesson

Sensors 2022, 22(14), 5092; https://doi.org/10.3390/s22145092 - 06 Jul 2022

Cited by 1 | Viewed by 1710

Abstract

Sidescan sonar is a small and low-cost sensor that can be mounted on most unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs). It has the advantages of high resolution and wide coverage, which could be valuable in providing an efficient and cost-effective [...] Read more.

Sidescan sonar is a small and low-cost sensor that can be mounted on most unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs). It has the advantages of high resolution and wide coverage, which could be valuable in providing an efficient and cost-effective solution for obtaining the bathymetry when bathymetric data are unavailable. This work proposes a method of reconstructing bathymetry using only sidescan data from large-scale surveys by formulating the problem as a global optimization, where a Sinusoidal Representation Network (SIREN) is used to represent the bathymetry and the albedo and the beam profile are jointly estimated based on a Lambertian scattering model. The assessment of the proposed method is conducted by comparing the reconstructed bathymetry with the bathymetric data collected with a high-resolution multi-beam echo sounder (MBES). An error of 20 cm on the bathymetry is achieved from a large-scale survey. The proposed method proved to be an effective way to reconstruct bathymetry from sidescan sonar data when high-accuracy positioning is available. This could be of great use for applications such as surface vehicles with Global Navigation Satellite System (GNSS) to obtain high-quality bathymetry in shallow water or small autonomous underwater vehicles (AUVs) if simultaneous localization and mapping (SLAM) can be applied to correct the navigation estimate. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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16 pages, 17730 KiB

Open AccessCommunication

A System for Autonomous Seaweed Farm Inspection with an Underwater Robot

by Ivan Stenius, John Folkesson, Sriharsha Bhat, Christopher Iliffe Sprague, Li Ling, Özer Özkahraman, Nils Bore, Zheng Cong, Josefine Severholt, Carl Ljung, Anna Arnwald, Ignacio Torroba, Fredrik Gröndahl and Jean-Baptiste Thomas

Sensors 2022, 22(13), 5064; https://doi.org/10.3390/s22135064 - 05 Jul 2022

Cited by 10 | Viewed by 4778

Abstract

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges [...] Read more.

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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19 pages, 4962 KiB

Open AccessArticle

Cooperative Control of Underwater Vehicle–Manipulator Systems Based on the SDC Method

by Aleksey Kabanov, Vadim Kramar, Ivan Lipko and Kirill Dementiev

Sensors 2022, 22(13), 5038; https://doi.org/10.3390/s22135038 - 04 Jul 2022

Cited by 4 | Viewed by 1854

Abstract

The paper considers the problem of cooperative control synthesis for a complex of

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underwater vehicle–manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models [...] Read more.

The paper considers the problem of cooperative control synthesis for a complex of

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underwater vehicle–manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models in the form of state space with state-dependent coefficients (SDC-form). That allowed us to apply methods of suboptimal control with feedback based on the state-dependent differential Riccati equation (SDDRE) solution at a finite time interval, providing the change in control intensity with the transient effect of the system matrices in SDC form. The paper reveals two approaches to system implementation: a general controller for the whole system and a set of

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independent subcontrollers for UVMSs. The results of both approaches are similar; however, for the systems with a small number of manipulators, the common structure is recommended, and for the systems with a large number of manipulators, the approach with independent subcontrollers may be more acceptable. The proposed method of cooperative control was tested on the task of cooperative control for two UVMSs with six-link manipulators Orion 7R. The simulation results are presented in the article and show the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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21 pages, 10059 KiB

Open AccessArticle

A Dynamically Reconfigurable Autonomous Underwater Robot for Karst Exploration: Design and Experiment

by Tho Dang, Lionel Lapierre, Rene Zapata, Benoit Ropars and Guillaume Gourmelen

Sensors 2022, 22(9), 3379; https://doi.org/10.3390/s22093379 - 28 Apr 2022

Cited by 3 | Viewed by 2110

Abstract

This paper presents the design and experiment of an autonomous underwater robot which can change the geometric configuration of its actuators, according to mission requirements or environmental constraints. The robot consists of two subsystems: forward part with three thrusters and backward part with [...] Read more.

This paper presents the design and experiment of an autonomous underwater robot which can change the geometric configuration of its actuators, according to mission requirements or environmental constraints. The robot consists of two subsystems: forward part with three thrusters and backward part with four thrusters. The position and orientation of these thrusters can be dynamically changed during missions. Being different from most of other reconfigurable underwater robots which were designed as linked-modules, our robot has a unified design. It is suitable for specific mission in confined environments (e.g., karst exploration) in which the robot has to modify its shape to go through a narrow section or align the most part of its thrusters in the direction of a strong current, for examples. The design procedure, from hardware to software, of the robot is presented and experimental results are shown to demonstrate the versatility of the robot. Furthermore, the discussion and comparison between our robot and other underwater robots with adaptable actuation geometry are presented to highlight advantages of our design. Finally, the idea of using our robot for classic docking problem, which has some common features with karst exploration requirements in using dynamically reconfigurable robots, is discussed. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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