Ayaka Kume

AUV Tri-TON — A hover-capable platform for 3D visualization of complicated surfaces

AUV Tri-TON is a hovering type autonomous underwater vehicle developed by the University of Tokyo, launched in 2011. The vehicle was constructed as a testbed under the governmental project to develop instruments to estimate ore reserves in underwater hydrothermal deposits. The vehicles mission is to obtain dense, large-area 3D image of hydrothermal vent fields, in collaboration with a seafloor station. The information will be also used for environmental assessments, mine planning, and educational activities. Although the vehicle is not equipped with an inertial navigation system (INS), the vehicle can estimate its position in real-time with a precision enough for rough photo-mosaicking, owing to the mutual acoustic positioning with the station. The vehicle has two suites of imaging instruments looking forward and downward directions in order to image whole surface of bumpy hydrothermal vent fields. The vehicle has been tested through a series of experiments at tanks and real fields. In April 2012 the vehicle was deployed to the hydrothermal vent field of Kagoshima Bay in Japan and succeeded in observing seafloor with the area of around 200 square meters.

Autonomous detection and volume determination of tubeworm colonies from underwater robotic surveys

Although the vast amount of information collected by AUVs brings significant benefit to oceanographic research, it is necessary to develop methods to analyze the large volumes of data, in order to avoid accumulation of unused information. Automatic data processing and analysis are key technologies necessary to cope with this problem. We propose a robust, automated method for detection and volume determination of tubeworm colonies using visual and geometric features obtained during underwater robotic surveys, on the condition that the position of the sensors are provided. The tubeworm is a characteristic benthos of hydrothermal vent fields. The proposed method achieves robustness against sensor noise by using both geometric and visual features for identification. First, the tubeworm candidates are obtained as a three-dimensional region between the measured bathymetry of the region and an estimation of the seafloor topology without tubeworms. Next, the tubeworm candidates are verified through frequency analysis of corresponding images. The performance of this method was verified using a data set obtained by the AUV Tri-Dog 1 at Tagiri vent field, Kagoshima bay in Japan.

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

Autonomous underwater vehicles (AUVs) can operate without the need for human control or tether cables as long as there is sufficient energy. AUVs have recently been used for seafloor imaging. Visual observation by AUVs provides high-resolution color information of the seafloor. However, conventional observation techniques that follow a prespecified path offer limited coverage because it is impossible for operators to build a suitable path in unknown rough terrain. A flawed prespecified path will produce incomplete observation. If unobserved areas are found during postprocessing, another dive is necessary, which increases the total cost. To overcome this problem, the authors have proposed a path replanning method to realize high-coverage observation in one dive. With this method, the AUV evaluates unobserved areas after the first prespecified observation; if unobserved areas are found, the AUV recreates an appropriate path to cover what was missed. The validity of the proposed method was previously evaluated using an artificial target in a tank and in shallow seas at a depth of approximately 35 m. In this study, the feasibility of the method was validated in a more challenging setting: experimental data were taken from a hydrothermal vent field in Kagoshima Bay, Japan.

Long-term quantitative observation of tubeworm colonies using an AUV

This paper proposes a comprehensive method to measure three dimensional distribution of tubeworms, a representative benthos of hydrothermal vent fields, for thousands of square meters by an autonomous underwater vehicle (AUV). Firstly, the AUV takes pictures and generates fine bathymetry of the seafloor through dense, low altitude scanning. The AUV can obtain accurate position in real-time with the aid of artificial landmarks. The three dimensional area of tubeworm colonies is then obtained using both the topological and visual features of seafloor. Finally, the quantitative value such as area, volume and average height of the colonies are obtained. The AUV Tri-Dog 1, a hovering type testbed, has been periodically deployed to the Tagiri vent field, Kagoshima Bay in Japan since 2006. The depth is around 100m and there exists the worlds shallowest tubeworm colonies. This paper reports the spatial and temporal distribution of the tubeworms revealed by series of deployments.

Field experimental results of path re-planning method for an AUV to visualize complicated surface in 3D

Although AUVs can observe seafloor without tether cables nor human control, it is impossible for operators to confirm in real time that there are no unscanned areas caused by occlusions, positioning errors, and so on. If unobserved areas are found by post processing, another deployment of the AUV is necessary to cover them. Therefore, we have proposed a recursive observation method of rough terrain by an AUV to realize a high coverage observation at one deployment. In the method, the AUV evaluates unobserved regions after completing the preplanned path. After that, the vehicle generates a new path to cover them. This paper reports the results of sea experiments held at Suruga Bay in Japan, in November 2012. An artificial target was set up on the seafloor. Then the AUV Tri-TON observed the target based on the proposed method. As the generated path was concentrated on the target, the performance of the proposed method at the real sea environment was verified. In addition, mapping accuracy was evaluated by comparing the observational results and ground truth.