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Toward technological contributions to remote operations in the decommissioning of the Fukushima Daiichi Nuclear Power Station

川端 邦明

Japanese Journal of Applied Physics, 59(5), p.050501_1 - 050501_9, 2020/05




羽成 敏秀; 古川原 崚; 土田 佳裕; 川端 邦明; 千葉 悠介

JAEA-Review 2019-047, 32 Pages, 2020/03


楢葉遠隔技術開発センターは、東京電力ホールディングスが実施する福島第一原子力発電所の廃炉作業に資するため、遠隔操作機器・装置による廃炉作業の実証試験・要素試験が実施できる施設・設備を有している。2018年度は64件の施設利用を支援し、福島第一原子力発電所廃炉作業等の遠隔技術開発に貢献した。また福島県内企業・大学 廃炉・災害対応ロボット関連技術展示実演会等の開催に協力し、地域活性化・福島県の産業復興に貢献するとともに、第3回廃炉創造ロボコンや廃炉実習等の支援を通じて、長期に亘る福島第一原子力発電所の廃炉関連業務を担う次世代の人材育成に貢献した。本報告書は、2018年度における楢葉遠隔技術開発センターの施設・設備の整備・利用状況およびそれに係る取組み、遠隔基盤技術の開発状況、緊急時対応遠隔操作資機材の整備・訓練等の活動状況について取りまとめたものである。


An Image selection method from image sequence collected by remotely operated robot for efficient 3D reconstruction

羽成 敏秀; 川端 邦明; 中村 啓太*; 成瀬 継太郎*

Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.242 - 245, 2020/02



A Learning data collection using a simulator for point cloud based identification system

谷藤 祐太; 川端 邦明

Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.246 - 249, 2020/02

In this paper, we describe a method of acquiring learning data 3D point cloud data as learning data for deep learning using a simulator. Generally, a lot of data is necessary for building classifiers by deep learning approach. By using a simulator, various measurement conditions can be set thus, it is expected to collect variety of data for building high performance classifier. Data collection was conducted by virtual measurement using a mobile robot model and a sensor model. As a feasibility study of evaluating classification performance, we performed a simple identification experiment to confirm performance and applicability to actual measurement data. As a result, a high identification rate of 89 percent to three categories was obtained.


Fast in-situ mesh generation using Orb-SLAM2 and OpenMVS

Wright, T.*; 羽成 敏秀; 川端 邦明; Lennox, B.*

Proceedings of 17th International Conference on Ubiquitous Robots (UR 2020) (Internet), p.315 - 321, 2020/00

In exploratory robotics for nuclear decommissioning, environmental understanding is key. Sites such as Fukushima Daiichi Power Station and Sellafield often use manually controlled or semi-autonomous vehicles for exploration and monitoring of assets. In many cases, robots have limited sensing capabilities such as a single camera to provide video to the operators. These limitations can cause issues, where a lack of data about the environment and limited understanding of depth within the image can lead to a mis-understanding of asset state or potential damage being caused to the robot or environment. This work aims to aid operators by using the limited sensors provided i.e. a single monocular camera, to allow estimates of the robot's surrounding environments to be generated in situ without having to off load large amounts of data for processing. This information can then be displayed as a mesh and manipulated in 3D to improve the operator awareness. Due to the target environment for operation being radioactive, speed is prioritised over accuracy, due to the damaging effects radiation can cause to electronics. In well lit environments images can be overlaid onto the meshes to improve the operators understanding and add detail to the mesh. From the results it has been found that 3D meshes of an environment/object can be generated in an acceptable time frame, less than 5 minutes. This differs from many current methods which require offline processing due to heavy computational requirement of Photogrammetry, or are far less informative giving data as raw point clouds, which can be hard to interpret. The proposed technique allows for lower resolution meshes good enough for avoiding collisions within an environment to be generated during a mission due to it's speed of generation, however there are still several issues which need to be solved before such a technique is ready for deployment.



川端 邦明; 大隅 久*; 大西 献*

日本機械学会誌, 122(1211), p.16 - 17, 2019/10

本稿では、2019年5月にJヴィレッジホテルにおいて開催されたInternational Topical Workshop on Fukushima Decommissioning Research (FDR2019)のうち、われわれが担当したTrack3; Robot technology, remote control systemにおいて企画されたキーノートスピーチやテクニカルセッション関する報告および遠隔操作技術の周辺状況についての解説を行った。


Development of a GUI-based operation system for building a 3D point cloud classifier

谷藤 祐太; 川端 邦明; 羽成 敏秀

Proceedings of 2019 IEEE Region Ten Conference (TENCON 2019) (Internet), p.36 - 40, 2019/10

This paper describes a Graphical User Interface (GUI) based operation system for building a classifier based on deep learning and verifying its categorization performance. Currently, we build a structure discrimination method based on deep learning with 3D point cloud to support status awareness of the operator of remotely controlled robot. For building a powerful classifier, the operations like "collection of learning data", "construction of architecture" and "creation of learning model "are done by trial and error. Therefore, we consider to develop a system to make such complicated operations easier and more efficiently. In this paper, we describe about required functions for helping such operations and explain developed a prototype system in detail.


3D environment reconstruction based on images obtained by reconnaissance task in Fukushima Daiichi Nuclear Power Station

羽成 敏秀; 川端 邦明

E-Journal of Advanced Maintenance (Internet), 11(2), p.99 - 105, 2019/09

福島第一原子力発電所の廃炉作業において、作業を円滑に進めるために原子炉建屋内の状態を把握することは重要である。本発表では、廃炉作業の作業環境を認識するために時系列画像に基づいた環境の立体復元について報告する。立体復元手法であるStructure from Motion (SfM)を用いて、東京電力ホールディングスのHP上で公開されている原子炉格納容器(PCV)内の調査動画から得られた時系列画像に対して立体復元を試みた。視認性向上のため、SfMにより得られた3次元形状データに対してMulti-View Stereo (MVS)を用いた後処理も併せて行った。その結果、SfMにより時系列画像から構造物の部分的な立体復元が可能であること、MVSにより視認性の向上が可能であることが示された。


ロボットの実証試験; 楢葉遠隔技術開発センター

羽成 敏秀; 川端 邦明

金属, 89(7), p.582 - 588, 2019/07



Designing test methods for running capabilities of ground robots for nuclear disaster response

川端 邦明; 山田 大地; 白崎 令人; 石山 博紀

Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2019) (USB Flash Drive), p.559 - 564, 2019/07

This paper describes the development of some test methods for the robots utilized for nuclear disaster response and decommissioning tasks. From the experiences in the disaster response at Fukushima Daiichi Nuclear Power Station (FDNPS), we have learned the importances of the development of the robots and the improvement of the operation capability. In this paper, we describe the test methods for running performances of the robots that are fundamental functions for the tasks in the nuclear disaster response. As the typical examples, we describe the tests for the performance of running through the narrow passage, climbing up and down the stairs and running with dragging the cable.


Development of a robot simulator for remote operations for nuclear decommissioning

川端 邦明; 鈴木 健太

Proceedings of 16th International Conference on Ubiquitous Robots (UR 2019) (USB Flash Drive), p.501 - 504, 2019/06

This paper describes about the current development status of a robot simulator for remote operations for nuclear decommissioning. This simulator is designed for operator proficiency training and a robot development. We implemented plug-in functions that are required to simulate the situations and the conditions for task execution for decommissioning and also utilities for enhancing convenience for the users. We describe the current prototype of the robot simulator and such implemented functions and utilities. Functional details were also introduced and illustrated in this paper.


A Structure discrimination method by deep learning with point cloud data

谷藤 祐太; 川端 邦明

Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05

This paper describes about the development of an environment recognition method with point cloud data collected in a dark place like Fukushima Daiichi Nuclear Power Station (FDNPS). We reported the results of a feasibility study of the structure discriminations from LiDAR 3D point cloud data by a deep learning approach. Proposed method utilizes the image data of projected 3D point cloud as input for the classifier instead of coordinate data of 3D points directly. This idea realized to make shorten the learning time without large capacity of the memory for the computations. We selected five kinds of structures (Stairs, Pipe, Grating, Switchboard and Valve) commonly appeared in the general plant as a discrimination subjects for evaluating proposed method. As a result, the classifier showed an accuracy of 99.6% to five categories and we could confirm the validity of proposed method for the structure discrimination.


3D structural reconstruction based on images obtained by survey task for decommissioning

羽成 敏秀; 川端 邦明

Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05

福島第一原子力発電所の廃炉作業において、作業を円滑に進めるために原子炉建屋内の状態を把握することは重要である。本発表では、福島第一原子力発電所で撮影された画像から復元された立体モデルの視認性を改善する方法について報告する。提案手法は、画像の品質を改善する前処理、立体復元を行うStructure from Motion (SfM)、視認性を向上させた立体モデルを生成する後処理の3つのプロセスから構成されている。提案手法を用いて、東京電力ホールディングスのHP上で公開されている水中ROVによる原子炉格納容器(PCV)内の調査動画から得られた時系列画像に対して立体復元を試みた。その結果から、時系列画像から構造物の大まかな立体復元が可能であることが確認できた。



松田 朝陽*; 高橋 悟*; 川端 邦明; 尾田 正二*; 金子 俊一*

電気学会論文誌,D, 139(4), p.424 - 432, 2019/04

In recent years, biology researchers elucidate the biological and behavioral mechanisms based on measurement of observation data. Then, it is accompanied with time cost and misrecognition to measure a trajectory of creature from observation data based on human eyes. Therefore, biology researchers require automated measurement and recording support system. In this paper, we introduce a new method of swimming trajectory generation for analyzing of medaka behavior. Then, in order to recognize medaka behavior a method which combines orientation code matching by rotation invariant multiple-templates and particle filter based on multiple-likelihood functions is introduced. Through experimentations we prove the effectiveness of our method.


Development of a multi-copter simulator and a projection system for virtual operation experience

鈴木 健太; 川端 邦明

Proceedings of 2019 IEEE/SICE International Symposium on System Integration (SII 2019) (USB Flash Drive), 6 Pages, 2019/01

Our motivation is to utilize simulation technology to accelerate the decommissioning of Fukushima Daiichi Nuclear Power Station (FDNPS) by remote operated robots. We already developed several simulation functions in our previous work. Recently multi-copter was utilized for reconnaissance tasks at FDNPS. This paper described to design a simulation function for multi-copter operation training. Fluid dynamics affected to flying body are simulated by implemented function. We also attempt the demonstration of immersive operation environment based on a 3D projection system that provides virtual operation experience.


Image reconstruction of radioactive contamination due to the Fukushima-Daiichi Nuclear Power Station Accident using a compact Compton camera

佐藤 優樹; 寺阪 祐太; 宮村 浩子; 冠城 雅晃; 谷藤 祐太; 川端 邦明; 鳥居 建男

Reactor Dosimetry; 16th International Symposium on Reactor Dosimetry (ISRD-16) (ASTM STP 1608), p.428 - 436, 2018/11

 被引用回数:0 パーセンタイル:100

We developed a lightweight compact Compton camera to measure the distribution of radioactive contamination inside the Fukushima Daiichi Nuclear Power Station. We conducted performance evaluation tests in the coastal area of Fukushima, Japan, using the camera, which employs a cerium (Ce)-doped GAGG (Gd$$_{3}$$Al$$_{2}$$Ga$$_{3}$$O$$_{12}$$) scintillator coupled with a multipixel photon counter. The camera can clearly visualize spreading of radioactivity along the ground surface. In addition, we performed three-dimensional image reconstruction of the distribution of radioactive contamination using the multi-angle data obtained with the Compton camera. We succeeded in obtaining a three-dimensional image of radioactive contamination in the outdoor area.


Radiation imaging using a compact Compton camera inside the Fukushima Daiichi Nuclear Power Station building

佐藤 優樹; 谷藤 祐太; 寺阪 祐太; 宇佐美 博士; 冠城 雅晃; 川端 邦明; 宇津木 弥*; 菊地 弘幸*; 高平 史郎*; 鳥居 建男

Journal of Nuclear Science and Technology, 55(9), p.965 - 970, 2018/09

 被引用回数:7 パーセンタイル:10.12(Nuclear Science & Technology)

The Fukushima Daiichi Nuclear Power Station (FDNPS), operated by Tokyo Electric Power Company Holdings, Inc., went into meltdown after the occurrence of a large tsunami caused by the Great East Japan Earthquake of March 11, 2011. The radiation distribution measurements inside the FDNPS buildings are indispensable to execute decommissioning tasks in the reactor buildings. We conducted the radiation imaging experiment inside the turbine building of Unit 3 of the FDNPS using a compact Compton camera, and succeeded in visualizing the high-dose contamination (up to 3.5 mSv/h). We also drew a three-dimensional radiation distribution map inside the turbine building by integrating the radiation image resulting from the Compton camera into the point cloud data of the experimental environment acquired using the scanning laser range finder. The radiation distribution map shows the position of these contaminations on the real space image of the turbine building. The radiation distribution map helps workers to easily recognize the radioactive contamination and to decrease the radiation exposure; the contamination cannot be observed with the naked eye, naturally.



川端 邦明

日本ロボット学会誌, 36(7), p.460 - 463, 2018/09



Towards enhancement of test facilities for supporting nuclear decommissioning by remote technology

川端 邦明; 毛利 文昭*; 白崎 令人; 谷藤 祐太; 羽成 敏秀

Proceedings of 2017 IEEE/SICE International Symposium on System Integration (SII 2017), p.450 - 455, 2018/02



Remote radiation imaging system using a compact $$gamma$$-ray imager mounted on a multicopter drone

佐藤 優樹; 小澤 慎吾*; 寺阪 祐太; 冠城 雅晃; 谷藤 祐太; 川端 邦明; 宮村 浩子; 和泉 良*; 鈴木 敏和*; 鳥居 建男

Journal of Nuclear Science and Technology, 55(1), p.90 - 96, 2018/01

 被引用回数:11 パーセンタイル:4.67(Nuclear Science & Technology)

A remote radiation imaging system comprising a lightweight Compton camera and a multicopter drone was developed to remotely and quickly measure radioactive contamination inside the buildings of the Fukushima Daiichi Nuclear Power Station (FDNPS). The drone system is used for measuring detailed radiation distributions in narrow areas, which have been difficult to gauge with conventional aircraft monitoring using helicopters. A measurement of radiation distributions in outdoor environments in the coastal areas of Fukushima, Japan, was performed. The drone system with the Compton camera succeeded in remote observations of dense hotspots from the sky over a contaminated area near the FDNPS. The time required for image reconstruction is approximately 550 s in the case of a 9-m flight altitude for the hotspots with a surface dose rate of several tens of $$mu$$Sv/h. This drone system will be used inside the buildings of the FDNPS for remote measurement of radioactive contamination.

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