Marine Robotics for Deep-Sea Specimen Collection: A Systematic Review of Underwater Grippers

The collection of delicate deep-sea specimens of biological interest with remotely operated vehicle (ROV) industrial grippers and tools is a long and expensive procedure. Industrial grippers were originally designed for heavy manipulation tasks, while sampling specimens requires dexterity and precision. We describe the grippers and tools commonly used in underwater sampling for scientific purposes, systematically review the state of the art of research in underwater gripping technologies, and identify design trends. We discuss the possibility of executing typical manipulations of sampling procedures with commonly used grippers and research prototypes. Our results indicate that commonly used grippers ensure that the basic actions either of gripping or caging are possible, and their functionality is extended by holding proper tools. Moreover, the approach of the research status seems to have changed its focus in recent years: from the demonstration of the validity of a specific technology (actuation, transmission, sensing) for marine applications, to the solution of specific needs of underwater manipulation. Finally, we summarize the environmental and operational requirements that should be considered in the design of an underwater gripper.

Robot submarino: estado del arte y diseño

En los últimos años las investigaciones del área de mecatrónica están enfocados a la exploración marina, por lo que muchos centros de investigaciones y escuelas han encaminado sus trabajos al desarrollo de robots submarinos no tripulados y operados remotamente, también conocidos como ROUV por su acrónimo en inglés Remotely Operated Underwater Vehicle (Vehículo submarino operado remotamente). Este artículo muestra la historia de los vehículos operados remotamente ROV (Remotely Operated Vehicle), así como la clasificación, una breve descripción y aplicación de los robots submarinos no tripulados que han trascendido en el área de la mecatrónica. Por último, se describe el diseño de un robot submarino no tripulado en 3D para la exploración marina.

Operational failure assessment of Remotely Operated Vehicle (ROV) in harsh offshore environments

For an effective integrity assessment of marine robotic in offshore environments, the elements’ failure characteristics need to be understood. A structured probabilistic methodology is proposed for the operational failure assessment (OFA) characteristics of ROV. The first step is to assess the likely failure mode of the ROV system and its support systems. This captures the interaction and failure induced events during operation. The identified potential failure modes are further developed into logical connectivity based on the cause-effect relationship. The logical framework is modeled using the fault tree analysis technique to predict the ROV operational failure probability in an uncertain harsh environment. The fault tree analysis captured the logical relationship between the primary, intermediate, and top events probability. The importance measure criteria were adopted to identify the most probable events, links, and their importance on the failure propagation. The model was demonstrated with an ROV for deep arctic water subsea operations. The result identified the control system, communication linkages, human factor, among others, as most critical in the ROV operational failure. The methodology’s application provides core information on the Mean time between failure (MTBF) of the ROV system that could aid integrity management and provides a guide on early remedial action against total failure.

Deep oceanic submarine fieldwork with undergraduate students: an immersive experience with the Minerve software

We present the content and scripting of an active tectonic lab session conceived for third-year undergraduate students studying Earth sciences at Observatoire des Sciences de l'Univers in Lyon. This session is based on a research project conducted on the submarine Roseau active fault in the Lesser Antilles. The fault morphology is particularly interesting to map as this structure in the deep ocean is preserved from weathering. Thus, high-resolution models computed from remotely operated vehicle (ROV) videos provide exceptional educational material to link fault morphology and coseismic displacement. This class includes mapping exercises on geographical information systems and virtual fieldwork to provide basic understanding of active tectonics and active fault morphology in particular. The work has been conducted either in a full remote configuration via 3D online models or in virtual reality (VR) in a dedicated room using the Minerve software. During the VR sessions, students were either alone in the VR environment or participated as a group that included the instructor (physically in the classroom or remotely from another location), which is to our knowledge one of the first attempts of this kind in France. We discuss the efficiency of virtual fieldwork using VR based on feedback from teachers and students. We conclude that VR is a promising tool to learn observational skills in Earth sciences, subject to certain improvements that should be possible in the years to come.

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.

SET POINT TRACKING CONTROL OF A REMOTELY OPERATED VEHICLE USING MODEL PREDICTIVE CONTROL

This paper considers kinematics and dynamics of Remotely Operated Underwater Vehicle (ROV) to control position, orientation and velocity of the vehicle. Cascade control technique has been applied in this paper. The pole placement technique is used in inner loop of kinematics to stabilize the vehicle motions. Model Predictive control is proposed and applied in outer loop of vehicle dynamics to maintain position and velocity trajectories of ROV. Simulation results carried out on ROV shows the good performance and stability are achieved by using MPC algorithm, whereas sliding mode control loses its stability when ocean currents are high. Implementation of proposed MPC algorithm and stabilization of vehicle motions is the main contribution in this paper.

CONTROL OF REMOTELY OPERATED UNDERWATER VEHICLE USING MODEL PREDICTIVE CONTROL

This paper focuses on application of model predictive control on attitude control of remotely operated underwater vehicle. These vehicles are used in scientific, defence and oceanography applications. Remotely Operated Vehicle (ROV) considered in this paper is nonlinear model and complex. MPC is applied on ROV model to track in desired set point trajectories in the presence of uncertainties. Simulation has been carried out in MATLAB environment. Model Predictive Control has given significantly good results compared to PID, Adaptive and Variable structure control.

Deep-Sea Fish Fauna on the Seamounts of Southern Japan with Taxonomic Notes on the Observed Species

Several volcanic islands and submarine volcanoes exist in the sea connecting the Izu-Bonin Islands with the Mariana Islands, with trenches and islands formed by the submergence of the Pacific Plate under the Philippine Sea Plate. Although designated as a Marine Protected Area (MPA) in December 2020, the seamounts’ biodiversity has not been sufficiently researched. Therefore, direct observations and specimen sampling were conducted on four seamounts in this area using a remotely operated vehicle (ROV), autonomous underwater vehicle (AUV), and baited cameras (BCs). The ROV survey was conducted for 2–4 days on each seamount and divided into shallow and deep areas. During the expedition, 20 orders and 51 families of 81 deep-sea fish species were observed, including several potentially undescribed species, new genus or species records from Japanese waters, new depth records, new ecological information, and several rare fishes. The fish fauna and biodiversity abundance clearly differed among the seamounts; the seamount with a hydrothermal vent had the lowest diversity among the four seamounts. In shallow water, 23, 7, and 12 species were recorded only by ROV, AUV, and BC, respectively, indicating that combining these methods is beneficial for understanding the fish fauna of seamounts.