Tamaki Ura

Institute of Electrical and Electronics Engineers

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Co-Authors

Takashi Sakamaki

Tohoku University

Blair Thornton

University of Southampton

Toshihiro Maki

The University of Tokyo

Kazuo Ishii

Kurume University

Junichi Kojima

KDDI Research (Japan)

Akira Asada

Kobe University

Rajendar Bahl

Indian Institute of Technology Delhi

Yuya Nishida

Juntendo University

Harumi Sugimatsu

The University of Tokyo

Adrian Bodenmann

University of Southampton

Cooperative Institutions

Institute of Electrical and Electronics Engineers

114

The University of Tokyo

112

Gorgias Press (United States)

Japan Agency for Marine-Earth Science and Technology

Conference Board

Kyushu Institute of Technology

Tokyo University of Science

Advanced Institute of Industrial Technology

Hokkaido University

Tohoku University

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2A2-D02 Investigation of Marine Benthic Resources using Autonomous Underwater Vehicle in the sea of Okhotsk : 1st report: A proposal of Image Observation and Fish Identification Method

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) (2015)

Yuya NISHID Tamaki Ura Tomonori Hamatsu Kenji Nagahashi Shogo Inaba

This paper proposed a novel approach to the kichiji rockfish recognition method for investigation of the fish biomass. This method does not depend on the size, position and attitude of the fish, because SIFT feature with rotation invariant is used and is represented by vector quantization histogram. The experiment results showed that our method detected 94 % of object which is the fish from 37 photographs and recognized 57 % of the fish from detected image. And our method was able to recognize kichiji rockfish from SIFT features of the fish head. However, if more than one object is piled up, our method is a possibility of mistaken recognition because features of object other than target are used for recognition

Scale-invariant feature transform

Rockfish

10.1299/jsmermd.2015._2a2-d02_1

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Citations (0)

Advanced Navigation Method of Autonomous Underwater Vehicle using Artificial Underwater Landmark and Deadreckoning.

Journal of the Robotics Society of Japan (2002)

Son‐Cheol Yu Tamaki Ura Teruo Fujii Hayato Kondo

Recently, investigation of underwater structure and fresh water environment such as lake and dam which are closely related with human life have got much attention. Even though Autonomous Underwater Vehicles (AUVs) are anticipated to be very useful tools for mentioned missions, problems of conventional navigation method prevent AUVs from accomplishing them. In this paper, a new underwater navigation method for AUVs is proposed based on Artificial Underwater Landmarks (AULs) which are recognized by a vision system implemented in the robot. When an AUV cruises by deadreckoning, position error accumulated gradually depending on characteristics and direction of navigation devices reliability. When the error is within an acceptable range for finding a specific AUL along the track line, the AUV can correct the position and direction by refering to those of the AUL. Then, the AUV continues to cruise by deadreckoning again. Since the underwater vision system is requested to have high reliability, a new un-derwater image processing strategy is introduced based on analysis of underwater vision environment. Configuration of the AUL, shape and color of targets on it are designed to optimize image processing and recognition. Experiments were carried out to demonstrate the efficiency of the proposed navigation method by the intelligent AUV “Tri-Dog1”.

Landmark

Machine Vision

Navigation System

Position (finance)

10.7210/jrsj.20.290

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Citations (1)

Identification of Motion of Underwater Robot with Neural Network

Journal of the Society of Naval Architects of Japan (1993)

Tamaki Ura Kazuo Ishii

A structure of neural network which includes two kinds of recurrent connections, i. e., from the output layer to the input layer and from the hidden layer to the input layer, is proposed to represent the dynamics of a plant. The inputs consist of state variables of the plant and control signals, and the outputs are the state variables at the next time step. On the advantage of neural network, the property of the dymamic system with significant non-linearity can be represented by neural network based on learning of I/O data of the plant. A learning process to construct a network covering a wide area of the input domain is introduced and tested by applying this structure to the longitudinal motion of a test-bed vehile of crusing type underwater robots. It is shown that the system identification of high quality can be accomplished through the proposed simple scheme.

Identification

Echo state network

10.2534/jjasnaoe1968.1993.174_887

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Citations (1)

Vision Based Object Following for Underwater Vehicles

Journal of the Society of Naval Architects of Japan (1998)

A. Balasuriya Tamaki Ura

This paper proposes a vision based guidance system for Underwater Vehicles (UVs) to follow moving targets by collaborating with a pan and tilt camera mechanism. The proposed system can be divided into three main sections. The vision section provides the position information of the UV with respect to the object being tracked. A commercially available CCD camera mounted on the pan and tilt mechanism attached to the UV provides the environmental information as a two-dimensional image and the proposed vision algorithm detects the position of the interested target in that image. By the pan and tilt calibration, a transformation matrix is derived for the image pixel to pan/tilt angle relationship. The second section consisting of the pan and tilt mechanism, brings the interested object in the image to a predefined pixel position. The pan action is derived from the horizontal pixel movement and the tilt action from the vertical pixel movement. As a result of this action, the camera unit will be looking in a direction other than the UVs forward direction. The third section consisting of the UV controller, aligns the camera unit in the forward direction of the UV by controlling the vehicle. An experiment is conducted to demonstrate the performance of the proposed algorithm using the testbed UV available at the University of Tokyo called the Twin-Burger 2. The vision algorithm was implemented on the MaxVideo 250 image processing board. On these hardware, the proposed system successfully managed to track objects moving with a maximum speed of 4 cm/sec.

Position (finance)

Tilt sensor

Testbed

10.2534/jjasnaoe1968.1998.455

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Citations (1)

Development of coral reef monitoring system with autonomous underwater robot : 3rd report : Portable autonomous buoy system for underwater positioning

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) (2002)

Keisuke Watanabe Kazuo Ishii Akira Nakamura Tamaki Ura

Buoy

10.1299/jsmermd.2002.16_6

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1P1-B02 Mapping the Inside of a Water Pipe with an Underwater Robot

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) (2009)

Adrian Bodenmann Blair Thornton Tamaki Ura Unnikrishnan V. Painumgal

In this project a program that is capable of generating one image of an entire water pipe's inside wall was developed. An Autonomous Underwater Vehicle (AUV) equipped with a fish-eye camera will be deployed to dive through the pipe and take pictures of its inside wall. A computer program was implemented that maps such pictures to the 3 dimensional model of the water pipe and based on that, generates the image that one would see if the photographed section of pipe was cut along the side and rolled out. A second algorithm uses the rolled out versions of a series of consecutively taken photos to generate a mosaic showing the entire pipe in a single picture.

Section (typography)

10.1299/jsmermd.2009._1p1-b02_1

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Autonomous Landing of Underwater Vehicles Using High-Resolution Bathymetry

IEEE Journal of Oceanic Engineering (2019)

Mehul Sangekar Blair Thornton Adrian Bodenmann Tamaki Ura

The ability to land on the seafloor expands the envelope of tasks that underwater vehicles can carry out during survey and inspection. However, even though remotely operated vehicles routinely land during their operations, autonomous underwater vehicles (AUVs) lack the sensing and data processing capabilities needed to identify safe, stable landing sites. Here, an algorithm is developed that uses millimeter-resolution (mm-resolution) bathymetry to detect regions where an AUV of known geometry can safely and stably land on the seafloor. The algorithm uses physical models that consider vehicle geometry, seafloor slope, roughness, friction, and currents. It can identify the most suitable of multiple candidate sites based on a landing cost function. The performance of the algorithm is evaluated using seafloor bathymetry data that was obtained using an AUV equipped with a high-resolution laser mapping system on the slopes of the Takuyo Daigo seamount in the Northwest Pacific. The algorithm successfully identified multiple landing sites along a 500-m transect on the slopes of the surveyed seamount. The study demonstrates that safe, reliable AUV landing operation is feasible in actual seafloor environments.

Seafloor Spreading

Seamount

10.1109/joe.2019.2946923

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Citations (13)

2003A-GS4-2 Fuel-Optimal Guidance and Motion Control of AUV under Environmental Disturbances

Kangsoo Kim Tamaki Ura

Source

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Real time path-planning of an AUV based on characteristics of passive acoustic landmarks for visual mapping of shallow vent fields

Oceans (2007)

Toshihiro Maki Hayato Mizushima Hayato Kondo Tamaki Ura Takashi Sakamaki

Although underwater vent fields are of great scientific interest, accurate visual mapping is difficult because of the presence of bubble plumes that degrade the accuracy of conventional acoustic positioning systems such as long base line (LBL) and super short base line (SSBL). The authors had proposed a visual mapping method of shallow vent fields with an autonomous underwater vehicle (AUV) equipped with a profiling sonar, where positioning is based on vertical rod-shaped acoustic reflectors and bubble plumes. Although performance was verified through a series of experiments, there remain two challenges as follows. Firstly, observation is terminated if the vehicle was surrounded by bubble plumes, since the vehicle tries to avoid collision with not only artificial reflectors but also collision-safe bubble plumes. Secondly, the observation area drifts by disturbance since the waypoints are defined relative to the vehicle's position after descending close to seafloor. This paper proposes a real-time path planning method of an AUV as a part of the proposed observation method. The path of the vehicle is defined based on the types of landmarks as well as the geometric relationship between the vehicle and the landmarks. The vehicle can distinguish landmark types using a sheet laser and a camera. The proposed method was implemented on the AUV Tri-Dog 1 and a series of experiments were carried out in order to verify its performance.

Landmark

Field of view

10.1109/oceans.2007.4449321

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Citations (26)

Virtual Mooring Buoy “ABA” for Multiple Autonomous Underwater Vehicles Operation

Journal of Robotics and Mechatronics (2016)

Yuya Nishida Junichi Kojima Yuzuru Itoh Kenkichi Tamura Harumi Sugimatsu

[abstFig src='/00280001/09.jpg' width=""300"" text='Virtual Mooring Buoy ""ABA""' ]We developed a virtual mooring buoy, called an autonomous buoy “ABA” for AUV, which navigates the sea using thrusters in 2014. Its purpose was to deploy autonomous underwater vehicles (AUVs) effectively in seafloor exploration. The ABA tracks and positions AUVs operating underwater using onboard acoustic positioning. We have deployed the ABA with two type AUVs in seafloor exploration. That conducted in February 2015 used the ABA with one AUV to survey and inventory mineral deposits, demonstrating the ABA’s capability of positioning an AUV navigating at 1,370–1,550 m depths at a standard deviation of 100 m or less and tracking it while maintaining a horizontal distance of 50 m or less. The ABA navigated the AUV within a position error of about 10 m.

Buoy

Mooring

Seafloor Spreading

10.20965/jrm.2016.p0086

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Citations (4)