研究者詳細 - 安川　真輔

論文 - 安川　真輔

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Development of Remotely Operated Vehicle for Small-size Jellyfish Extermination and its Evaluation of Extermination Motion Control 査読有り国際誌

Hiroyuki Yokota, Shinsuke Yasukawa, Jonghyun Ahn

Proceedings of 2022 International Conference on Artificial Life and Robotics 27 797 - 802 2022年01月

施設園芸を対象としたトマト果実自動収穫ロボットの開発招待有り査読有り

藤永拓矢, 安川真輔, 石井和男

日本ロボット学会誌 ( 一般社団法人日本ロボット学会 ) 39 ( 10 ) 921 - 925 2021年12月

小型クラゲ駆除を目的とした自律型水中ロボットの開発および駆除装置の性能評価査読有り

安鍾賢, 筑紫彰太, 安川真輔, 園田隆

設計工学 ( 公益社団法人日本設計工学会 ) 57 ( 4 ) 181 - 190 2021年10月

記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>In recent years, extermination work has been carried out due to the increase in the number of jellyfish. In this research, we design and develop an AUV (Autonomous Underwater Vehicle) and conduct a jellyfish crushing experiment for the purpose of automating jellyfish extermination work. In the design and development of an AUV, electronic parts to operate autonomously were selected, and each electronic part was placed inside the hull to create a system diagram of them. Regarding the jellyfish extermination device, we designed and developed a device that sucks and crushes jellyfish together with water from the suction port. In this experiment, a jellyfish extermination device was mounted on developed AUV, and a crushing experiment was conducted using a jellyfish sample which is made by water and gelatin. As a result, it was confirmed that a jellyfish sample with a diameter of about 7 cm and a height of about 11 cm could be crushed to small pieces, which has average of volume 2885.6 mm<sup>3</sup>, during less in about 8 seconds.</p>

DOI： 10.14953/jjsde.2021.2931

Kyutacar

CiNii Article

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/jjsde/advpub/0/advpub_2021.2931/_article/-char/ja

Development and Evaluation of a Tomato Fruit Suction Cutting Device 査読有り

Takuya Fujinaga, Shinsuke Yasukawa, Kazuo Ishii

2021 IEEE/SICE International Symposium on System Integrations (SII) ( IEEE/SICE ) 628 - 633 2021年01月

Evaluation of Tomato Fruit Harvestability for Robotic Harvesting 査読有り

Takuya Fujinaga, Shinsuke Yasukawa, Kazuo Ishii

2021 IEEE/SICE International Symposium on System Integrations (SII) ( IEEE/SICE ) 35 - 39 2021年01月

Underwater 3D Scanner Using RGB Laser Pattern 査読有り国際誌

Yuya Nishida, Shinsuke Yasukawa, Kazuo Ishii

2021 IEEE/SICE International Symposium on System Integrations (SII) ( IEEE/SICE ) 2021年01月

Underwater image reconstruction using convolutional auto-encoder 査読有り

Shinsuke Yasukawa, Sreeraman Srinivasa Raghura, Yuya Nishida, Kazuo Ishii

Proc. of ICAROB2021 262 - 265 2021年01月

Tomato-harvesting-robot competition towards smart agriculture 招待有り査読有り

Kazuo Ishii, Takayuki Matsuo, Yasunori Takemura, Takashi Sonoda, Yuya Nishida, Shinsuke Yasukawa, Takuya Fujinaga

Proc. of ICAROB2021 1 - 5 2021年01月

A Greenhouse Project toward Smart Agriculture 査読有り

Kazuo Ishii, Yuya Nishida, Shinsuke Yasukawa, Kanako Shirahashi, Yasunori Takemura, Takayuki Matsuo

Proc. of ICAROB2021 409 - 412 2021年01月

Tomato-Harvesting Robot Competition: Aims and Developed Robot of 6th Competitions 査読有り

Takayuki Matsuo, Yasunori Takemura, Takashi Sonoda, Yuya Nishida, Shinsuke Yasukawa, Kazuo Ishii

Proc. of ICAROB2021 397 - 400 2021年01月

Tomato growth state map for the automation of monitoring and harvesting 査読有り

Fujinaga T., Yasukawa S., Ishii K.

Journal of Robotics and Mechatronics 32 ( 6 ) 1279 - 1291 2020年12月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020, Fuji Technology Press. All rights reserved. To realize smart agriculture, we engaged in its system-atization, from monitoring to harvesting tomato fruits using robots. In this paper, we explain a method of generating a map of the tomato growth states to mon-itor the various stages of tomato fruits and decide a harvesting strategy for the robots. The tomato growth state map visualizes the relationship between the maturity stage, harvest time, and yield. We propose a generation method of the tomato growth state map, a recognition method of tomato fruits, and an estimation method of the growth states (maturity stages and harvest times). For tomato fruit recognition, we demonstrate that a simple machine learning method using a limited learning dataset and the optical properties of tomato fruits on infrared images exceeds more com-plex convolutional neural network, although the results depend on how the training dataset is created. For the estimation of the growth states, we conducted a survey of experienced farmers to quantify the maturity stages into six classifications and harvest times into three terms. The growth states were estimated based on the survey results. To verify the tomato growth state map, we conducted experiments in an ac-tual tomato greenhouse and herein report the results.

DOI： 10.20965/jrm.2020.p1279

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098498979&origin=inward

Robot competition for underwater technology researchers and students 査読有り

Yuya Nishida, Takashi Sonoda, Takayuki Matsuo, Shinsuke Yasukawa, Masanori Sato, Yasunori Takemura, Kazuo Ishii

Journal of Advances in Artificial Life Robotics 1 ( 1 ) 11 - 15 2020年06月

An Optical Image Transmission System for Deep Sea Creature Sampling Missions Using Autonomous Underwater Vehicle 査読有り

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

IEEE Journal of Oceanic Engineering 45 ( 2 ) 350 - 361 2020年04月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 1976-2012 IEEE. The exploration of oceans using autonomous underwater vehicles (AUVs) is necessary for activities, such as the sustainable management of fishery resources, extraction of seafloor minerals and energy resources, and inspection of underwater infrastructure. As the next step in ocean exploration, AUVs are expected to employ end-effectors to make physical contact with seafloor creatures and materials. We propose a scenario for realizing a sampling mission using an AUV that is equipped to sample marine life. In this scenario, the sampling AUV observes the seafloor while concurrently transmitting the observed images to a surface vessel for inspection by the AUV operators. If the received images show an object of interest, the object is selected as a candidate of sampling target by the operators, who send a sampling command to the AUV. After receiving the command, the AUV returns to the target area and attempts to sample it. In this paper, we propose a system for transmitting images of the seafloor as part of the sampling-mission scenario. The proposed image transmission system includes a process for enhancing images of the deep seafloor, a process for selecting interesting images, and processes for compressing and reconstructing images. The image enhancement process resolves imaging problems resulting from light attenuation, such as color attenuation and uneven illumination. The process for selecting interesting images selects those that contain interesting objects, such as marine life. The selection process prevents the transmission of meaningless images that contain only flat sand on the seafloor. The proposed image compression method, which is based on color depth compression, reduces the amount of data. The combined process of selecting an interesting image and compressing it reduces various problems in acoustic communication, such as low information density and data loss. Instead of an overall image, part of an overall image is transmitted by a set of data packet, and each received data packet is reconstructed onboard the vessel. Because of image compression, the colors of a reconstructed image differ from those of an enhanced image. However, the reconstructed image contains similar colors, and the structural similarity index was found to be 91.4% by evaluating images that were subjected to a 4-b color compression. The proposed image transmission system was tested in the Sea of Okhotsk, and these tests were performed four times in different sea areas (minimum depth 380 m, maximum depth 590 m). The results show that the size of the data for a single image was reduced by a factor of 18 using the proposed image compression process, with each image taking 3.7 s to be transmitted via an acoustic modem (20 kb/s). Of the automatically selected images, 63% contained marine life, and the total transmission success rate was 22%.

DOI： 10.1109/JOE.2018.2872500

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055860272&origin=inward

Field Experiments of Underwater Image Transmission for AUV 査読有り

Shinsuke Yasukawa, Yuya Nishida, Jonghyun Ahn, Takashi Sonoda, Kentaro Yanagise, Keisuke Watanabe, Kazuo Ishii

In Proceedings of the 2020 International Conference on Artificial Life and Robotics 171 - 174 2020年01月

Reports on the 7th Underwater Robot Festival in Kitakyushu 査読有り

Yuya Nishida, Takashi Sonoda, Takayuki Matsuo, Shinsuke Yasukawa, Masanori Sato, Yasunori Takemura, Kazuo Ishii

In Proceedings of the 2020 International Conference on Artificial Life and Robotics 110 - 113 2020年01月

Sea Trials for Benthos Sampling Using Autonomous Underwater Vehicle 査読有り

Yuya Nishida, Shinsuke Yasukawa, Takashi Sonoda, Keisuke Watanabe, Kazuo Ishii

In Proceedings of the 2020 International Conference on Artificial Life and Robotics 167 - 170 2020年01月

Evaluation of Underwater Vehicle's Self-Localization Based on Visual Odometry or Sensor Odometry 査読有り

Yoshiki Tanaka, Akio Semmyo, Yuya Nishida, Shinsuke Yasukawa, Jonghyun Ahn, Kazuo Ishii

In Proc. of the 14th IEEE International Conference on Industrial and Information Systems (ICIIS) 2019 1 - 6 2019年12月

Entropy-Based Exposure Time Setting for　an Adaptive Multi-Exposure Image Fusion 査読有り国際誌

Shinsuke YASUKAWA, Tomohiro NASUNO, Kazuo ISHII

In Proc. of the 14th IEEE International Conference on Industrial and Information Systems (ICIIS) 2019 1 - 6 2019年12月

Entropy-Based Exposure Time Setting for an Adaptive Multi-Exposure Image Fusion 査読有り

Yasukawa S., Nasuno T., Ishii K.

2019 IEEE 14th International Conference on Industrial and Information Systems: Engineering for Innovations for Industry 4.0, ICIIS 2019 - Proceedings 529 - 534 2019年12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）

© 2019 IEEE. We investigated the setting method of exposure times for an adaptive multi-exposure image fusion. Because the range of luminance in the natural environment is wide and objects in captured image have inherent optical properties, the vision system need to adapt to the input light. The proposed system comprises an image sensor module and a field-programmable gate array (FPGA) which is composed of control signal generator for an image sensor, HDR (High-dynamic-range) image generator/evaluator. In this study, we employed and evaluated maximum entropy of HDR image as the exposure time setting method.

DOI： 10.1109/ICIIS47346.2019.9063302

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084139742&origin=inward

Evaluation of underwater vehicle's self-localization based on visual odometry or sensor odometry 査読有り

Tanaka Y., Semmyo A., Nishida Y., Yasukawa S., Ahn J., Ishii K.

2019 IEEE 14th International Conference on Industrial and Information Systems: Engineering for Innovations for Industry 4.0, ICIIS 2019 - Proceedings 384 - 389 2019年12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）

© 2019 IEEE. This research focuses on two self-localization methods for observation of the sea floor and sampling. One is a method of estimate the self-localization using Kalman filter from the acceleration data calculated from equation of motion and the velocity data considering the effect of underwater vehicle's oscillation. The other is visual odometry using a stereo camera. The AUV was deployed in a sea area 40m depth to evaluate the performance of self-localization estimated by two methods. Self-localization estimation using Kalman filter was less accurate than visual odometry, but it was confirmed that the variance of the estimated velocity was smaller than before estimation. In the visual odometry using stereo camera, it was confirmed that the estimation error depends on the travel direction of AUV and the moving direction of a stereo camera.

DOI： 10.1109/ICIIS47346.2019.9063345

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084155438&origin=inward

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