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羽成 敏秀; 中村 啓太*; 今渕 貴志; 川端 邦明
計測と制御(インターネット), 65(6), p.438 - 443, 2026/06
廃炉作業が進められている福島第一原子力発電所では高い放射線環境で様々な調査や作業を行うため、様々な遠隔技術が活用されている。廃炉作業環境下で用いられている遠隔技術においてオペレータの空間認知の不良性が問題視されており、それを改善する手法として作業環境の立体モデルの提示が有望視されている。本稿では、画像に基づいた立体モデリングおよびシミュレーション技術の活用やAIを利用した立体モデリング技術について紹介するとともに、廃炉作業への適用に関する課題についても述べる。
山田 大地; 鈴木 壮一郎; 伊藤 倫太郎; 太田 侑杏*; 金子 瑛一郎*; 大金 一二*; 川端 邦明
Advanced Robotics, 40(5), p.259 - 270, 2026/03
被引用回数：0 パーセンタイル：0.00(Robotics)本稿では狭隘空間におけるUASの開口部の上昇通過に関する性能評価方法について述べる。本研究は、技術的専門家とは限らないUASユーザにも理解しやすい評価を示すことにより、多様な場面でのUASの利用を促し、UAS産業の促進に貢献することを目的としている。本研究ではUASのユーザやメーカにとって受容しやすい性能評価方法を開発するために、研究の早期からユーザ・メーカとの意見交換を繰り返し実施しながら評価方法を開発した。また、実験により性能評価結果が開口部の上昇通過についてUASの性能の差を的確に示すことを確認した。本稿では、産業の促進を目的とした性能評価のアプローチ、ユーザ・メーカの意見を考慮する開発方法、UASの性能を比較する実験と考察について述べる。
今渕 貴志; 川端 邦明
Proceedings of 2026 IEEE/SICE International Symposium on System Integration (SII2026) (Internet), p.1105 - 1109, 2026/01
This paper describes a fully automated method for generating 3D pipe models with wall thickness from 3D point cloud data. In decommissioning of Fukushima Daiichi Nuclear Power Station, rapid 3D modeling of plant structures is essential for dose assessment and remote operation planning. In our method, pipe regions are first discriminated, then scanned along three orthogonal axes to extract pipe instances, followed by geometric fitting with wall thickness for use in shielding calculations. Finally, generated models are exported in Computer-Aided Design (CAD) and Building Information Modeling (BIM) formats for using facility management. Our method was validated on a 3D point cloud measured in a mock-up plant environment. We confirmed a high conversion rate and reduced processing time by restricting computation to pipe regions. The outputs models imported into existing software without errors.
谷藤 祐太; 今渕 貴志; 川端 邦明
Proceedings of the 31st International Symposium on Artificial Life and Robotics (AROB 31st 2026), p.1011 - 1016, 2026/01
福島第一原子力発電所(1F)の廃炉作業では、濁りや浮遊物、レンズの汚染、放射線起因ノイズなどにより劣化したロボットカメラ映像を用いて作業を行う必要があり、クリーンな参照画像は得られない。本研究では、Noise2Noise(N2N)によるノイズ除去とFSRCNNによる超解像を組み合わせた軽量な画像復元パイプラインを検討した。その結果、N2N→FSRCNNの構成が、NIQEおよびPIQEによる画質評価指標および主観評価の両面において、ノイズや不自然なテクスチャを抑制しつつ視認性を安定的に向上させることを確認した。
中島 慎介*; Wu, J.*; 羽成 敏秀; 今渕 貴志; 松日楽 信人*; 川端 邦明; An, Q.*; 山下 敦*
Proceedings of IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR2025), p.127 - 132, 2025/10
被引用回数：0 パーセンタイル：0.00(Automation & Control Systems)Fuel debris retrieval is the indispensable task for decommissioning Fukushima Daiichi Nuclear Power Station (FDNPS). One of the significant barriers is the high radiation which deteriorates the operation efficiency. The radiation makes the robot sensors and the electronics get out of order easily. For the acceleration of decommissioning operation, it is required to develop the DX (Digital Transformation) platform to evaluate the sensor performance in the radiation environment. This research presents the approach exploiting a photorealistic game engine. The developed DX platform contributes to evaluate the sensor degradation and image processing algorithm inside the PCV (Primary Container Vessel) of FDNPS.
今渕 貴志; 川端 邦明
Artificial Life and Robotics, 30(1), p.184 - 195, 2025/02
This paper describes a 3D point cloud segmentation pipeline that contributes to the efficiency of decommissioning works at the Fukushima Daiichi Nuclear Power Station. For decommissioning works, simulations and calculations for preliminary work planning using 3D structural models are crucial from a safety and efficiency viewpoint. However, 3D modeling works typically require high costs. Therefore, we aim to improve the efficiency of 3D modeling by segmenting geometric shape regions into categories in a 3D point cloud state using deep learning. Our pipeline uses 3D computer-aided design semantics to create a training dataset that reduces annotation costs and helps learn human knowledge. Performance evaluation results show that the discriminator can discriminate major structural categories with high accuracy using deep learning models. However, we confirm that even the state-of-the-art model has limitations in discriminating structures containing similar shapes between categories and structures in categories with a small number of training data. In the analysis of evaluation results, we discuss challenges encountered by our pipeline for practical applications.
今渕 貴志; 羽成 敏秀; 川端 邦明
Proceedings of 2025 IEEE/SICE International Symposium on System Integration (SII2025), p.1416 - 1421, 2025/01
This paper describes a 3D reconstruction based on grouping similar structures for the aim of generating 3D information for understanding the workspace from the images acquired inside the Primary Containment Vessel (PCV) of the Fukushima Daiichi Nuclear Power Station. In the decommissioning works, preliminary surveys are carried out in the PCV, and the workers need to understand the workspace from a large amount of camera images, which requires a great deal of effort. We are currently working on 3D reconstruction from camera images of the PCV, however, one of the challenges is to improve the visibility of reconstructed model containing noise and artifact. In this study, we propose a method of grouping similar structures on the image and utilizing predicted group labels for 3D reconstruction process to highlight structures shapes and to refine 3D modeling. Our key idea is to perform unsupervised segmentation for grouping similar structures that are suitable for images acquired in the PCV because they are difficult to assign correct semantics for unclear structures and the few learning resources. We show on the reasonable performance of our method by validating it by video images of a typical plant environment and survey videos of the PCV taken under adverse conditions such as radiation noise.
羽成 敏秀; 中村 啓太*; 今渕 貴志; 川端 邦明
Journal of Robotics and Mechatronics, 36(6), p.1537 - 1549, 2024/12
本稿では、時系列画像から3次元モデルを効率的に生成するための画像選択法を導入した立体復元処理について述べる。効率的な立体復元に適した画像を得るために、時系列画像中の冗長画像を除去する画像選択法の適用を試みた。提案手法では、オプティカルフローと固定しきい値に基づいて、時系列画像から適切な画像を選択する。その結果、提案手法により、カメラで取得した時系列画像に基づく立体復元処理の計算量を削減することができた。その結果、立体復元精度を一定に保ったまま、立体復元処理の計算量を削減できることを確認した。
中島 慎介*; Moro, A.*; 小松 廉*; Faragasso, A.*; 松日楽 信人*; Woo, H.*; 川端 邦明; 山下 淳*; 淺間 一*
Proceedings of 2024 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR2024), p.160 - 165, 2024/11
Fuel debris retrieval is the fundamental task for decommissioning Fukushima Daiichi (1F). The presence of high radiation intensively affects the performance of conventional robotic systems which in many cases, due to failure of the electronic components, become obstacles to be retrieved. New systems and techniques are required to cope with the current limitations and find efficient solutions. This research presents the design approach exploiting rapid prototyping of a gripper system for a manipulator used to retrieve fuel debris on the bottom of the PCV (Primary Container Vessel). Modeling of the gripper is performed in simulations to find the relations between the ligaments that constrain the bending motion. The debris simulants and the fabricated 3D-printed gripper are shown.
中島 慎介*; Moro, A.*; 小松 廉*; Faragasso, A.*; 松日楽 信人*; Woo, H.*; 川端 邦明; 山下 淳*; 淺間 一*
Proceedings of International Topical Workshop on Fukushima Decommissioning Research 2024 (FDR2024) (Internet), 4 Pages, 2024/10
Fuel debris retrieval at the bottom of the primary containment vessel (PCV) is one of the significant tasks for the decommissioning of the nuclear power plant and in particular for 1F. It is challenging for conventional manipulators to perform the retrieval process due to the presence of radiation, water leakage, and poor lighting conditions. We tackle those problems with the design and fabrication of a novel mechanical manipulator and its control and navigation algorithm. Continuous Variable Transmission (CVT)-based actuation improves the robot's shock resistance. AI-based navigation algorithm enables semiautonomous navigation and grasping in the cluttered environment inside the PCV.
谷藤 祐太; 羽成 敏秀; 川端 邦明
Proceedings of International Topical Workshop on Fukushima Decommissioning Research 2024 (FDR2024) (Internet), 3 Pages, 2024/10
In this paper, we describe the results of a feasibility study of a noise reduction method from images using deep learning technology for decommissioning work. Currently, remotely operated robots have been used for the decommissioning work at the Fukushima Daiichi Nuclear Power Station (FDNPS) due to the high radiation environment. We have been conducting research and development for providing clear images during operations by removing only noise from images containing noise to contribute to safe and secure decommissioning work. Since we do a feasibility study of the noise reduction method using deep learning, the main target is not the video, but rather images, which are components of the video. We adopted the approach of building a learning model that can cope with various types of noise by training many noisy images in the deep learning process.
中村 啓太*; 馬場 啓多*; 渡部 有隆*; 羽成 敏秀; 松本 拓*; 今渕 貴志; 川端 邦明
Artificial Life and Robotics, 29(4), p.546 - 556, 2024/09
This paper describes a method for integrating multiple dense point clouds using a shared landmark to generate a single real-scale integrated result for photogrammetry. It is difficult to integrate high-density point clouds reconstructed by photogrammetry because the scale differs with each photogrammetry. To solve this problem, this study places a QR code of known sizes, which is a shared landmark, in the reconstruction target environment and divides the reconstruction target environment based on the position of the QR code that is placed. Then, photogrammetry is performed for each divided environment to obtain each high-density point cloud. Finally, we propose a method of scaling each high-density point cloud based on the size of the QR code and aligning each high-density point cloud as a single high-point cloud by partial-to-partial registration. To verify the effectiveness of the method, this paper compares the results obtained by applying all images to photogrammetry with those obtained by the proposed method in terms of accuracy and computation time. In this verification, ideal images generated by simulation and images obtained in real environments are applied to photogrammetry. We clarify the relationship between the number of divided environments, the accuracy of the reconstruction result, and the computation time required for the reconstruction.
川端 邦明; 今渕 貴志; 白崎 令人*; 鈴木 壮一郎; 伊藤 倫太郎; 青木 勇斗; 大森 崇純
ROBOMECH Journal (Internet), 11, p.11_1 - 11_11, 2024/09
This paper describes a measuring unit to realize the synchronous collection of air dose rate and measurement position for efficient dosimetry survey and data logging in a working space. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and a dosimetry part, which are integrated into a single measurement unit through an embedded computer that installs a ROS (Robot Operating System) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and the measurement position. In this paper, we describe the prototype system configuration and the experimental results obtained in the mockup test space and nuclear facility to discuss its performance.
中村 啓太*; 羽成 敏秀; 今渕 貴志; 川端 邦明
Proceedings of 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2024), p.7 - 8, 2024/07
写真測量法は、対象物を撮影した複数の画像から3次元的に復元する技術である。実際の撮影では、対象物を撮影するカメラの画角に振動が発生し、写真測量に適した画像を撮影できず、対象物を復元できない場合がある。そこで、この振動をシミュレーション上で乱数を用いて実装し、振動の大きさが写真測量法で得られる復元結果に与える影響を検証した。検証の結果、振動の大きさと3次元復元の成功率との関係が示された。
松本 拓; 羽成 敏秀; 川端 邦明; 中村 啓太*; 八代 大*
Artificial Life and Robotics, 29(2), p.358 - 371, 2024/05
This paper describes a three-dimensional (3D) modeling method for sequentially and spatially understanding situations in unknown environments from an image sequence acquired from a camera. The proposed method chronologically divides the image sequence into sub-image sequences by the number of images, generates local 3D models from the sub-image sequences by the Structure from Motion and Multi-View Stereo (SfM-MVS), and integrates the models. Images in each sub-image sequence partially overlap with previous and subsequent sub-image sequences. The local 3D models are integrated into a 3D model using transformation parameters computed from a camera trajectory estimated by the SfM-MVS. In our experiment, we quantitatively compared the quality of integrated models with a 3D model generated from all images in a batch and the computational time to obtain these models using three real data sets acquired from a camera. Consequently, the proposed method can generate a quality integrated model that is compared with a 3D model using all images in a batch by the SfM-MVS and reduce the computational time.
横村 亮太*; 後藤 雅貴*; 吉田 健人*; 割澤 伸一*; 羽成 敏秀; 川端 邦明; 福井 類*
IEEE Robotics and Automation Letters (Internet), 9(4), p.3275 - 3282, 2024/04
被引用回数：2 パーセンタイル：19.97(Robotics)廃炉作業におけるロボットの遠隔操作のエラーを低減するため、作業環境を常時観察できるRail DRAGONを開発した。Rail DRAGONは、原子炉格納容器(PCV)内に長尺の軌道構造体(レールモジュール)を組み立てて押し込み、そのレール上に複数台のモニタリングロボットを繰り返し配置することで構築され、高放射線環境下での常時監視を可能にしたものである。特に、Rail DRAGONの構成要素である屈曲可能なレールモジュール、直線状のレールモジュール、基部ユニット、モニタリングロボットを開発した。具体的には、可搬性・作業性に優れた超長尺多関節構造物の実現手法を提案・実証している。また、処分を考慮しつつ、容易に展開・交換が可能な観測機器の展開手法を提案し、その実現可能性を検証する。
中村 啓太; 羽成 敏秀; 松本 拓; 川端 邦明; 八代 大*
Journal of Robotics and Mechatronics, 36(1), p.115 - 124, 2024/02
During the decommissioning activities, a movie was shot inside the reactor building during the investigation of the primary containment vessel by applying photogrammetry, which is one of the methods for three-dimensional (3D) reconstruction from images, to the images from this movie, it is feasible to perform 3D reconstruction of the environment around the primary containment vessel. However, the images from this movie may not be suitable for 3D reconstruction because they were shot remotely by robots owing to limited illumination, high-dose environments, etc. Moreover, photogrammetry has the disadvantage of easily changing 3D reconstruction results by simply changing the shooting conditions. Therefore, this study investigated the accuracy of the 3D reconstruction results obtained by photogrammetry with changes in the camera angle of view under shooting conditions. In particular, we adopted 3D computer graphics software to simulate shooting target objects for 3D reconstruction in a dark environment while illuminating them with light for application in decommissioning activities. The experimental results obtained by applying artificial images generated by simulation to the photogrammetry method showed that more accurate 3D reconstruction results can be obtained when the camera angle of view is neither too wide nor too narrow when the target objects are shot and surrounded. However, the results showed that the accuracy of the obtained results is low during linear trajectory shooting when the camera angle of view is wide.
今渕 貴志; 川端 邦明
Journal of Robotics and Mechatronics, 36(1), p.63 - 70, 2024/02
In the decommissioning of Fukushima Daiichi Nuclear Power Station, radiation dose calculations using a 3D model of the workspace are performed to determine appropriate measures to reduce exposure. However, constructing a 3D model from 3D point cloud is costly. In order to separate the geometrical shape regions on 3D point cloud, we have been developing the structure discrimination method by 3D and 2D deep learning for contributing to 3D modeling automation technology. In this paper, we describe a method for transferring and fusing labels to handle 2D prediction label in 3D space. We propose an exhaustive label fusion method for plant facilities with intricate structures. In evaluation, we applied the method to a mock-up plant dataset and confirmed that it works effectively.
今渕 貴志; 川端 邦明
Proceedings of 2024 IEEE/SICE International Symposium on System Integration (SII2024) (Internet), p.141 - 146, 2024/01
This paper describes a method for volumetric-based semantic 3D modeling from 3D point cloud obtained in a plant environment. In order to calculate a radiation dose distribution of the workspace in decommissioning, the shape, arrangement, materials, and thicknesses of structures are essentially required in addition to dose values. However, it is costly to create such enriched 3D models from 3D point cloud. In this study, we propose a method to create 3D models with structural category and material thickness by combining 2D image-based deep learning and volumetric reconstruction method. To discriminate structures, structural category labels are predicted by a pre-trained 2D semantic segmentation network on projected image created from 3D point cloud. Then, a triangular mesh is generated from the integrated Truncated Signed Distance Function (TSDF) according to prediction labels. In addition, we optimize the TSDF thickness assignment function to reduce surface distance error. Our evaluation reports thickness and surface distance errors when generating meshes with three different structural categories in a mock-up plant environment.
馬場 啓多*; 渡部 有隆*; 中村 啓太*; 松本 拓; 羽成 敏秀; 川端 邦明
Proceedings of 29th International Symposium on Artificial Life and Robotics (AROB 29th 2024) (Internet), p.751 - 756, 2024/01
This study proposes a partial-to-partial point cloud registration method based on estimated parameters in photogrammetry and QR code. Some research and development on Generating a 3D map of the workspace by photogrammetric methods have been proposed for the decommissioning work at the Fukushima Daiichi Nuclear Power Plant. Photogrammetry is a method for 3D reconstruction of the location and shape of target objects from many images, and the processing time depends on the number of images. Considering the reconstruction of a large area, the number of images increases, and processing time also increases significantly. To reduce such computational time, this study considers applying SfM-MVS (Structure from Motion and Multi-View Stereo), which is one of the photogrammetry methods, to each segmented image group, aligning each obtained result, integrating them, and creating a model of the entire space. This alignment is called partial-to-partial registration and it is difficult to find the correspondence points for registration. Therefore, we place markers such as QR codes in the target reconstruction space to make it easy to find the correspondence points. We adopt the QR code as a 2D code because it is easy to reconstruct by photogrammetry. In this paper, we discuss the validity of this approach by comparing it with the integrated model using all images applying SfM-MVS. We verify the validation of the proposed method by simulation due to the large number of images and the ease of modifying the environment. The experiment about varying the number of image divisions shows that the reconstruction result from all images is more accurate than the integrated result. However, all of these models have high reconstruction accuracy. Moreover, the accuracy of the integrated model does not depend on the number of divisions.
