研究者詳細 - 石井　和男

その他リンク： https://ci.nii.ac.jp/naid/130007551777

視覚情報に基づくトマト果実収穫ロボットの行動戦略（第四報）:―トマト果実の熟度及び位置情報のマッピング―

藤永拓矢, 安川真輔, 李冰賀, 石井和男

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 1P1 - A05 2018年01月

担当区分：最終著者,　責任著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

This paper presents a generation method of tomato growth state map that has maturity and position of tomato. The tomato growth state map is used for the tomato harvesting robot to automatically plan the harvesting movement. As a first step of generating the tomato growth state map, a mosaic image expressing the tomato cultivation environment is generated. Next, immature to mature tomato are recognized by recognition method using infrared image. Based on color and depth information, maturity of tomato is judged and tomato position is calculated. This information is added to the mosaic image, and the tomato growth state map is generated. 19 immature tomatoes and 7 mature tomatoes were automatically detected from tomato state growth map that generated from 10 images.

DOI： 10.1299/jsmermd.2018.1P1-A05

その他リンク： https://ci.nii.ac.jp/naid/130007551123

人間とロボットのマルチエージェントシステムに向けたフットサルにおける協調行動の解析

富永萌子, 武村泰範, 石井和男

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2P2 - L09 2018年01月

担当区分：最終著者,　責任著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

As technology develops, realization of a symbiotic society between human beings and robots sharing their environment has become one of most important subjects. For example, soccer is a multi-agent game that require strategies such as each members' position and defense. The behavior of a player changes depending on the game situation such as winning or losing, score gap, remaining time. The players may play offensive when their team is losing, or be defensive when their team is winning with minimum score difference. In this paper, human soccer game is observed and analyzed to determine how the state vector of each players affect team behavior. The team strategy is analyzed based on parameters such as the positions, the number players, score, time and actions of soccer robots using Self-Organizing Map(SOM)

DOI： 10.1299/jsmermd.2018.2P2-L09

その他リンク： https://ci.nii.ac.jp/naid/130007552222

フルカラー構造化光を用いた水中三次元計測:―第2報計測装置の開発および水中環境がおよぼす影響の検証―

進木智也, 西田祐也, 石井和男

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2P2 - C07 2018年01月

担当区分：最終著者,　責任著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

Rich undersea mineral resources are confirmed around Japan and the research about undersea mineral resources and deep sea creatures has been conducted actively. Since three-dimensional data could not be get by human, three-dimensional measurement technology is important in the deep sea survey. Therefore we designed a measuring device suitable for underwater environment.

DOI： 10.1299/jsmermd.2018.2P2-C07

その他リンク： https://ci.nii.ac.jp/naid/130007551964

Deep-sea image enhancement using multi-scale retinex with reverse color loss for autonomous underwater vehicles 査読有り国際誌

Mercado M., Ishii K., Ahn J.

OCEANS 2017 - Anchorage 2017-January 1 - 6 2017年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 Marine Technology Society. Advances on underwater optical imaging has been rampant recently because of numerous applications it offers and the increasing use of underwater vehicles. Unfortunately, due to the inherent property of light in water, images tend to be hazy, have non-uniform illumination and colors get distorted. Retinex algorithms were formulated for dynamic range compression and tonal rendition to fix the illumination and color distortion problem. Multi-scale Retinex, an extension of the original Retinex algorithm, has been proven to enhance images. However, Retinex Theory is based on human visual system in air medium. Due to this, light attenuation is not considered. Through Beer-Lambert Law, the light attenuation percentage can be estimated and added back to the original image in a process the researchers call Reverse Color Loss. Thus, combining Multi-scale Retinex and Reverse Color Loss, an enhancement algorithm for deep-sea application for the use of AUVs is proposed.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048121401&origin=inward

Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition 査読有り国際誌

Ahn J., Yasukawa S., Sonoda T., Ura T., Ishii K.

Journal of Marine Science and Technology (Japan) 22 ( 4 ) 758 - 770 2017年12月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017, JASNAOE. The underwater robot is one of the most important research tools in deep-sea exploration where the high pressure, extreme darkness, and radio attenuation prevent direct access. In particular, Autonomous Underwater Vehicles (AUVs) are the focus of much attention since they do not have tethered cables and can navigate freely. However, ideally, AUVs should be able to make independent decisions even with limited information using mounted sensors. That is, AUVs need powerful programming and low-power consumption computer systems which enable them to recognize their surroundings and cruise for long distances. Thus, modern computer development makes it possible for AUVs to be one of the most practical solutions for deep-sea exploration and investigations. As a next-generation AUV, we have been developing a Sampling-AUV that can dive into deep-sea regions and bring back samples of marine creatures in order to more fully understand the marine ecosystem. Our mission scenario calls for the Sampling-AUV to transmit deep-sea floor images to scientists on the research ship using acoustic communication. The scientists select the marine creatures to sample, and the AUV is tasked with retrieving them. The AUV then returns to the area where the interesting marine creatures have been observed, and collects and brings back samples. In order to realize this mission scenario, the sea-floor images need to be enhanced to assist the judgment of the scientists as the color red attenuates rapidly and the images become bluish while small differences in AUV altitude to the sea-floor also affect the brightness of the images due to light attenuation. Moreover, although underwater acoustic communication is slow and inaccurate, the AUV is required to select interesting images that include marine life. In this paper, we propose a deep-sea floor image enhancement method based on the Retinex theory and its performance was evaluated using deep-sea floor images taken by an AUV. The performance of the image enhancement was evaluated through crab recognition.

DOI： 10.1007/s00773-017-0442-1

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85015873175&origin=inward

Erratum to: Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition (Journal of Marine Science and Technology, (2017), 22, 4, (758-770), 10.1007/s00773-017-0442-1) 査読有り

Ahn J., Yasukawa S., Sonoda T., Ura T., Ishii K.

Journal of Marine Science and Technology (Japan) ( Journal of Marine Science and Technology (Japan) ) 22 ( 4 ) 2017年12月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

DOI： 10.1007/s00773-017-0448-8

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85017584395&origin=inward

Kinematics of Two-Roller-Driven Ball for RoboCup Soccer Robot 査読有り

Shota Chikushi, Tharindu Weerakoon, Takashi Sonoda, Kazuo Ishii

Journal of Robotics, Networking and Artificial Life 4 ( 3 ) 248 - 253 2017年12月

Automatic Extraction of Thickness Information from Sub-Surface Acoustic Measurements of Manganese Crusts 査読有り

Neettiyath U., Thornton B., Sangekar M., Ishii K., Sato T., Bodenmann A., Ura T.

OCEANS 2017 - Aberdeen 2017-October 1 - 7 2017年10月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 IEEE. Mapping and estimating the volumetric distribution of cobalt-rich manganese crusts (Mn-crust) is a challenging task that lies at the centre of deep-sea mineral prospecting. Acoustic methods are effective and capable of in-situ continuous measurements of Mn-crust thickness, providing much higher spatial resolutions compared to traditional methods involving sampling. However, processing acoustic signal in order to estimate thickness values is difficult due to low signal to noise ratios. This paper proposes a combination of image processing techniques in addition to acoustic signal processing in order to improve the accuracy of measurements. The advantage is the possibility of using the physical properties of Mn-crust, such as local continuity in order to recognize valid measurements. Testing the algorithm on data collected from sea experiments demonstrate that the reflected signals from the crust can be identified, resulting in spatially continuous thickness estimates.

DOI： 10.1109/OCEANSE.2017.8084917

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044632642&origin=inward

Sea-floor image transmission system for AUV 査読有り国際誌

Ahn J., Yasukawa S., Weerakoon T., Sonoda T., Nishida Y., Ura T., Ishii K.

OCEANS 2017 - Aberdeen 2017-October 1 - 6 2017年10月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 IEEE. Autonomous Underwater Vehicle (AUV) has become one of the promising tool for ocean exploration during the last few decades, and, in particular, is the solution for the spatial-temporal investigations in wide areas for a long period. One of the next mission expected from AUV is deep sea specimen sampling, which is currently performed by Remotely Operated Vehicle (ROV) or Human Occupied Vehicle (HOV) where the sampling targets are selected by scientists on-line. In order to establish the similar on-line investigation with AUV system, the sea-floor images have to be transmitted to the scientists on the support vessel by acoustic communication. However, the speed of the acoustic communication is low compared with that of radio communication, and the data can be lost because of the directionality of acoustic modem, the positional relationship between the AUV and the support vessel, attenuation and so on. The robust image transmission system is necessary with acoustic communication for in-situ decision making for sampling by AUV with many tasks. In this paper, we propose a sea-floor image transmission system with image compression, and evaluated by sea trials in Suruga-bay. The image compression method is based on a set of color palettes, where the colors of a color palette are assigned as a set of main colors obtained from the minimum variance quantization, to represents a typical sea-floor image. The colors of the obtained images are replaced by the most similar colors in the color palette. The images compressed by a 16-colors color palette are evaluated by Structural SIMilarity (SSIM) method, and these compressed images have shown the SSIM index of 88.5%. The duration of one image transmission is about 40 seconds in the sea trials and the transmission success rate is 75%.

DOI： 10.1109/OCEANSE.2017.8084926