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JAEA Reports

Development of semantic survey map building system using semi-autonomous mobile robots for surveying of disaster area and gathering of information in nuclear power station (Contract research); FY2019 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; Tokyo Polytechnic University*

JAEA-Review 2020-062, 47 Pages, 2021/01

JAEA-Review-2020-062.pdf:3.43MB

JAEA/CLADS had been conducting the Nuclear Energy Science & Technology and Human Resource Development Project in FY2019. Among the adopted proposals in FY2018, this report summarizes the research results of the "Development of Semantic Survey Map Building System using Semi-autonomous Mobile Robots for Surveying of Disaster Area and Gathering of Information in Nuclear Power Station" conducted in FY2019.

Journal Articles

Automatic data acquisition for visualizing radioactive substances by combining a gamma-ray imager and an autonomous mobile robot

Sato, Yuki; Minemoto, Kojiro*; Nemoto, Makoto*; Torii, Tatsuo

Journal of Instrumentation (Internet), 16(1), p.P01020_1 - P01020_18, 2021/01

 Times Cited Count:0 Percentile:0(Instruments & Instrumentation)

Journal Articles

Construction of virtual reality system for radiation working environment reproduced by gamma-ray imagers combined with SLAM technologies

Sato, Yuki; Minemoto, Kojiro*; Nemoto, Makoto*; Torii, Tatsuo

Nuclear Instruments and Methods in Physics Research A, 976, p.164286_1 - 164286_6, 2020/10

 Times Cited Count:6 Percentile:93.25(Instruments & Instrumentation)

JAEA Reports

Development of semantic survey map building system using semi-autonomous mobile robots for surveying of disaster area and gathering of information in nuclear power station (Contract research); FY2018 center of world intelligence project for nuclear science/technology and human resource development

Collaborative Laboratories for Advanced Decommissioning Science; Tokyo Polytechnic University*

JAEA-Review 2019-022, 35 Pages, 2020/01

JAEA-Review-2019-022.pdf:2.71MB

CLADS, JAEA, had been conducting the Center of World Intelligence Project for Nuclear Science/Technology and Human Resource Development (hereafter referred to "the Project") in FY2018. The Project aims to contribute to solving problems in nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station, Tokyo Electric Power Company Holdings, Inc. For this purpose, intelligence was collected from all over the world, and basic research and human resource development were promoted by closely integrating/collaborating knowledge and experiences in various fields beyond the barrier of conventional organizations and research fields. The sponsor of the Project was moved from the Ministry of Education, Culture, Sports, Science and Technology to JAEA since the newly adopted proposals in FY2018. On this occasion, JAEA constructed a new research system where JAEA-academia collaboration is reinforced and medium-to-long term research/development and human resource development contributing to the decommissioning are stably and consecutively implemented. Among the adopted proposals in FY2018, this report summarizes the research results of the Development of Semantic Survey Map Building System Using Semi-autonomous Mobile Robots for Surveying of Disaster Area and Gathering of Information in Nuclear Power Station. The objective of the present study is to research and develop semi-autonomous mobile robot systems (multi-sensor fusion system, semantic simultaneous localization and mapping (SLAM), system for traversable-route learning and safe traversable-route presentation, etc.) that simply, safely, and rapidly make semantic survey maps including multiple information (air dose rate, temperature, obstacles, etc.). The system will be applied to the investigation of the situation inside the building of the nuclear power station where people cannot access at the time of disaster.

Journal Articles

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

Wright, T.*; Hanari, Toshihide; Kawabata, Kuniaki; Lennox, B.*

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

Journal Articles

Development of fast reactor containment safety analysis code, CONTAIN-LMR, 3; Improvement of sodium-concrete reaction model

Kawaguchi, Munemichi; Doi, Daisuke; Seino, Hiroshi; Miyahara, Shinya

Proceedings of 23rd International Conference on Nuclear Engineering (ICONE-23) (DVD-ROM), 6 Pages, 2015/05

CONTAIN-LMR code is an integrated analysis tool to predict the consequence of severe accident in a liquid metal fast reactor. A sodium-concrete reaction is one of the most important phenomena, and Sodium-Limestone Concrete Ablation Model (SLAM) has been installed into the original CONTAIN code. The SLAM treats chemical reaction kinetics between the sodium and the concrete compositions mechanistically, the application is limited to the limestone concrete. In order to apply SLAM to the siliceous concrete which is an ordinary structural concrete in Japan, the chemical reaction kinetics model has been improved. The improved model was validated to analyze a series of sodium-concrete experiments which were conducted in Japan Atomic Energy Agency. It has been found that relatively good agreement between calculation and experimental results is obtained and the CONTAIN-LMR code has been validated with regard to the sodium-concrete reaction phenomena.

Oral presentation

Sensor database for research of SLAM in reactor buildings

Yamada, Taichi; Kawabata, Kuniaki

no journal, , 

In reactor buildings of Fukushima Daiichi Nuclear Power Plant (hereinafter referred as 1F), there are still many of uninvestigated areas due to the accident on March 11th, 2011. Because it is easy to assume that these uninvestigated areas are high radiation, remote control robots is used for investigations in reactor buildings. Thus, robot mapping technology called SLAM (Simultaneous Localization And Mapping) is one of the key technology for the decommissioning. This research aimed to construct the sensor database aimed to enhance SLAM research.

Oral presentation

Sensor database to test SLAM method for decommissioning of 1F

Yamada, Taichi; Kawabata, Kuniaki

no journal, , 

In reactor buildings of Fukushima Daiichi Nuclear Power Plant (hereinafter referred as 1F), there are still many of uninvestigated areas due to the accident on March 11th, 2011. Because it is easy to assume that these uninvestigated areas are high radiation, remote control robots is used for investigations in reactor buildings. Thus, robot mapping technology called SLAM (Simultaneous Localization And Mapping) is one of the key technology for the decommissioning. This research aimed to construct the sensor database aimed to enhance SLAM research.

Oral presentation

Evaluation method of robot mapping performance for decommissioning

Yamada, Taichi; Kawabata, Kuniaki

no journal, , 

For decommissioning of Fukushima Daiichi Nuclear Power Plant (1F), workers need to know about work site information for working efficiency and safety of themselves. Mapping technology in robotics called Simultaneous localization and mapping (SLAM) is useful for sharing the site information. Though, there are still technical problems for applying SLAM to 1F tough environments. This paper shows the development of the SLAM evaluation method aimed to enhance the research of SLAM for 1F.

Oral presentation

Development of dataset to evaluate SLAM for Fukushima Daiichi Nuclear Power Plant decommissioning

Yamada, Taichi; Kawabata, Kuniaki

no journal, , 

Simultaneous localization and mapping (SLAM) is not only a key technology for robot to move automatically, but also is a useful technology for human to understand the state of places. Especially for the site where access is constrained by something harmful such as Fukushima Daiichi Nuclear Power Plant (1F), understanding of the state of the site is important. In addition, mapping using remote control robots is one of the ideal solution for investigation such sites. However, there are technical problems to solve for applying SLAM to extreme environments, for example, how to obtain landmarks under severe environmental condition. Furthermore, for extreme environments, we have very limited or no chance for testing SLAM on the actual site, and this makes difficult to research for applying SLAM. For this reason, an evaluation method without testing on the actual 1F site is needed to promote research of SLAM for 1F. This paper introduces the development of the dataset for SLAM evaluation with the mockup field instead of the actual site, specifically the dataset on the mockup field of Primary Containment Vessel (PCV) platform under dark illumination.

Oral presentation

Oral presentation

Demonstration of radiation imaging at Fukushima Daiichi Nuclear Power Station

Sato, Yuki

no journal, , 

no abstracts in English

Oral presentation

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