Hovering Autonomous Underwater Vehicle Research Articles

Establishment of line-of-sight optical links between autonomous underwater vehicles: Field experiment and performance validation

Establishing a line-of-sight link between autonomous underwater vehicles (AUVs) is an unavoidable challenge for realizing high data rate optical communication in ocean exploration. We propose a method for link establishment by maintaining the relative position and orientation between AUVs. Using a reinforcement learning algorithm, we search for the policy that can suppress external disturbances and optimize the link establishment efficiency. To evaluate the performance of the proposed method, we prepared a hovering AUV to conduct the link establishment experiments. The reinforcement learning policy trained in a simulation environment was deployed on the AUV in real environments. In field experiments, our approach successfully performed the link establishment from the hovering AUV to an autonomous surface vehicle. Based on the experimental results, we evaluate the performance of the AUV in executing the link establishment policy. Comparisons with existing optical search-based link establishment methods are presented.

Investigation of a New Hovering Autonomous Underwater Vehicle for Underwater Missions

It is impossible to implement very tasks by diver, because of complexity of underwater environment and high pressure in the deep sea. These tasks can just be done by a vehicle that includes all special requirements such as: high maneuverability, precise controllability, and especially hovering capability. Underwater robots are integral parts of the industry and marine science. The application of the underwater vehicles has increased with the development of the activities in deep sea. This paper presents a special Hovering type Autonomous Underwater Vehicle (HAUV) for underwater missions. To provide the most suitable and efficient formation of vehicle thrusters for reduction of complexity of control strategies and control of the most degrees of freedom, in this paper, a new thrusters' configuration is investigated, in terms of number of the thrusters, position and the thrust direction of the thrusters. The state feedback controller is designed according to the linear dynamic model and then applied to the non-linear model to validate the controller performance. Designed controller consists of three controllers: horizontal plane controller, vertical plane controller and surge controller. The last controller is developed to control the forward speed. For examination of the system behavior in presence of environmental disturbance and hydrodynamic coefficients uncertainty, the robustness of controller is also investigated.

Collision Avoidance of External Obstacles for an Underwater Transportation System

Transportation using multiple autonomous vehicles with detection avoidance capability is useful for military applications. It is important for such systems to avoid collisions with underwater obstacles in an effective way, while keeping track of the target location. In this paper, sensor-based and path-planning methods of external collision avoidance were investigated for an underwater transportation system. In particular, sensor-based wall-following and hard-switching collision avoidance strategies and an offline RRT* path-planning method was implemented on the simulation model of the transportation system of four Hovering Autonomous Underwater Vehicles (HAUVs). Time-domain motion simulations were performed with each method and their ability to avoid obstacles was compared. The hard-switching method resulted in high yaw moments which caused the vehicle to travel towards the goal by a longer distance. Conversely, in the wall-following method, the yaw moment was kept to zero. Moreover, the wall-following method was found to be better than the hard-switching method in terms of time and power efficiency. The comparison between the offline RRT* path-planning and wall-following methods showed that the fuel efficiency of the former is higher whilst its time efficiency is poorer. The major drawback of RRT* is that it can only avoid the previously known obstacles. In future, offline RRT* and wall following can be blended for a better solution. The outcome of this paper provides guidance for the selection of the most appropriate method for collision avoidance for an underwater transportation system.

MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV

This paper presents a high performance (low computationally demanding) monocular vision-based system for a hovering Autonomous Underwater Vehicle (AUV) in the context of autonomous docking process-MViDO system: Monocular Vision-based Docking Operation aid. The MViDO consists of three sub-modules: a pose estimator, a tracker and a guidance sub-module. The system is based on a single camera and a three spherical color markers target that signal the docking station. The MViDO system allows the pose estimation of the three color markers even in situations of temporary occlusions, being also a system that rejects outliers and false detections. This paper also describes the design and implementation of the MViDO guidance module for the docking manoeuvres. We address the problem of driving the AUV to a docking station with the help of the visual markers detected by the on-board camera, and show that by adequately choosing the references for the linear degrees of freedom of the AUV, the AUV is conducted to the dock while keeping those markers in the field of view of the on-board camera. The main concepts behind the MViDO are provided and a complete characterization of the developed system is presented from the formal and experimental point of view. To test and evaluate the MViDO detector and pose an estimator module, we created a ground truth setup. To test and evaluate the tracker module we used the MARES AUV and the designed target in a four-meter tank. The performance of the proposed guidance law was tested on simulink/Matlab.

Centralized Control System Design for Underwater Transportation using two Hovering Autonomous Underwater Vehicles (HAUVs)

Abstract In this paper, a centralized control system is designed for the two HAUVs undertaking underwater transportation of a spherical payload via cylindrical manipulators. First, the nonlinear coupled dynamic model is developed considering the rigid body connection method for transportation. The effect of the hydrodynamic, hydrostatic and thrust parameters are taken about the centre of the combined body i.e. the centre of payload. Path trajectory is generated using the minimum snap trajectory algorithm. The trajectory is divided into segments for each directional motion which is further divided into the waypoints based on the time step of the duration. The path between two waypoints is represented by a 7th order polynomial. The centralized control system is designed to follow the desired trajectory. The control system is designed using PID controllers for the motion control in each direction. The main technical requirements are the stability of the payload, accurate trajectory tracking and robustness to overcome uncertainties. Stability cannot be compromised because of the rigid connection between the vehicles and the payload, whereas, tracking is given a tolerance of ±5%. Transportation task is observed for the desired motion in the horizontal plane. The time domain motion simulation results show that the desired trajectory has been accurately followed by the combined system while meeting the technical requirements.

Mapping underwater ship hulls using a model-assisted bundle adjustment framework

This paper reports on a model-assisted bundle adjustment (BA) framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectation–maximization (EM) class of algorithms, we introduce a hidden binary label for each visual feature that indicates if that feature is considered part of the nominal model, or if the feature corresponds to 3D structure that is absent from the model. Therefore, in addition to improved estimates of the feature locations, we can identify visual features that correspond to foreign structure on the ship hull. We show that this framework is a special case of the Gaussian max-mixtures framework, which can be efficiently incorporated into state-of-the-art graph-based simultaneous localization and mapping (SLAM) solvers.In addition, the precision of our bundle adjustment framework allows the identification of structural deviations between 3D structure inferred from bundle-adjusted camera imagery and the prior model. These structural deviations are clustered into shapes, which allow us to fuse camera-derived structure back into the 3D mesh. This augmented model can be used within a 3D photomosaicing pipeline, providing a visually intuitive 3D reconstruction of the ship hull. We evaluate our pipeline using the Bluefin Robotics hovering autonomous underwater vehicle (HAUV) surveying the SS Curtiss, where a 3D mesh derived from computer aided design (CAD) drawings serves as the prior model. In addition to more consistent visual reconstructions, we can update the prior mesh with 3D information corresponding to underwater structure, such as biofouling or manually-placed cylindrical shapes with known dimensions.

PID 제어기를 이용한 호버링 AUV의 구현과 자세 제어

An attitude controller for a 6-DOF hovering autonomous underwater vehicle (HAUV) is implemented. We add a vertical thruster, an underwater camera, a wireless communication device, and a DVL to the HAUV that was developed a year ago. The HAUV is composed of 5 thrusters, 2 servo-motors, and 4 apparatus parts. Two rotating thrusters control the surge, heave, and roll of the vehicle. The vertical thruster controls the pitch, and two horizontal thrusters control the sway and yaw of the vehicle. The HAUV’s movement in each direction is controlled by 6 PID controllers. Each PID controller controls the propulsive force and angle of a thruster. In a horizontal and vertical movement experiment, we showed the feasibility of the proposed controller by maintaining a given depth and heading angle of the HAUV.

회전팔 추진기를 가진 시험용 HAUV의 설계 및 구현

In this paper, we propose the hardware and software of a test-bed of a hovering AUV (autonomous underwater vehicle). Test-bed to develop as the underwater robot for the hovering -type is planning to apply for marine resource development and exploration for deep sea. The RTU that controls a azimuth thruster and a vertical thruster of test-bed is a intergrated-type thruster. The main control unit that collects sensor's data and performs high-speed processing and controls a movement of test-bed is a underwater hybrid navigation system. Also it transfers position, posture, state information of test-bed to the host PC of user using a wireless communication. The host PC checks a test-bed in real time by using a realtime monitoring system that is implemented by LabVIEW.

Thruster fault-tolerant control of a hovering AUV with four horizontal and two vertical thrusters

This paper deals with the motion control of a hovering autonomous underwater vehicle (AUV) with four horizontal and two vertical thrusters, when one or more thrusters are completely malfunctioned. Three thruster fault cases are considered: one horizontal thruster is faulty; two horizontal thrusters are faulty; and one vertical thruster is faulty. Through a series of simulations and an experiment, it is validated that the AUV can track a planned path in a 3-D space with minimally three thrusters (two are horizontal ones); however, some cases require the changes of the vehicle’s preferred direction of motion and its movement manner. Additionally, when the number of active horizontal thrusters is less than the required degree-of-freedom, a continuous state feedback control law does not exist due to the non-holonomic constraint. This paper highlights that the hovering AUV can overcome the non-holonomic constraint if using the feature that their translational and rotational motions can be controlled independently.

Active planning for underwater inspection and the benefit of adaptivity

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from an exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.

Design and Deployment of a Four-Degrees-of-Freedom Hovering Autonomous Underwater Vehicle for sub-Ice Exploration and Mapping

This paper describes the 2008 and 2009 Antarctic deployments of the National Aeronautics and Space Administration ENDURANCE autonomous underwater vehicle (AUV). The goal of this project was to conduct three autonomous tasks beneath the ice cap 4 m thick of West Lake Bonney: first, to measure the three-dimensional (3D) water chemistry of the lake at prespecified coordinates; second, to map the underwater face of the Taylor Glacier; third, to chart the bathymetry of the lake bottom. At the end of each mission the AUV had to locate and return through a hole in the ice slightly larger than the outer diameter of the vehicle. During two 10-week deployments to Antarctica, in the austral summers of 2008 and 2009, ENDURANCE logged 243 h of sub-ice operational time, conducted 275 aqueous chemistry sonde casts, completed a 3D bathymetry survey over an area of 1.06 km2 at a resolution of 22 cm, and traversed 74 km beneath the ice cap of West Lake Bonney. Many of the characteristics and capabilities of ENDURANCE are similar to the behaviours that will be needed for sub-ice autonomous probes to Europa, Enceladus, and other outer-planet icy moons. These characteristics are also of great utility for terrestrial operations in which there is a need for an underwater vehicle to manoeuvre precisely to desired positions in 3D space or to manoeuvre and explore complicated 3D environments.

Autonomous Exploration and Mapping of Flooded Sinkholes

In this paper, we describe the control, navigation, and mapping methods that were developed for a hovering autonomous underwater vehicle that explored flooded cenotes in Mexico. The cenotes of Sistema Zacatón in Tamaulipas, Mexico are flooded sinkholes, exotic geological formations with unique water chemistry. The largest, Zacatón, is over 300-m deep. None of the cenotes were mapped before the present DEPTHX project. The goals of the DEPTHX project were to construct metrically accurate three-dimensional maps of the cenotes, and to collect environmental data, imagery, water samples, and core samples. The unknown extent of the cenotes, together with the challenging scientific mission, spurred the development of a robotic vehicle that autonomously, with no communications to the surface, built accurate three-dimensional maps using sonar and collected a variety of scientific data, including core samples from the cenote walls. In this paper, we describe the design, implementation, and testing of the robot software, as well as the results from mapping four cenotes of Sistema Zacatón.