Challenge to investigation of fuel debris in RPV by an advanced super dragon articulated robot arm (Contract research); FY2020 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; Tokyo Institute of Technology*

JAEA-Review 2021-045, 65 Pages, 2022/01

JAEA-Review-2021-045.pdf:3.41MB

The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), had been conducting the Nuclear Energy Science & Technology and Human Resource Development Project (hereafter referred to "the Project") in FY2020. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station, Tokyo Electric Power Company Holdings, Inc. (TEPCO). 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 FY2019, this report summarizes the research results of the "Challenge to investigation of fuel debris in RPV by an advanced super dragon articulated robot arm" conducted in FY2020. The present study aims to develop the implementation techniques of the remote sensing method on a robot arm for monitoring the structure status in the reactor and the distribution of nuclear materials by a long-articulated robot arm with controlling and grasping the position and posture of the robot arm hand. In FY 2020, we have conducted fundamental operation check of the robot arm in the simulated environment, prototype construction of telescopic articulated arm and cable storage mechanism, investigation of drive wire specifications, improvement of LIBS probe, prototype construction …

Study for estimation of snow depth by using DSM made by SfM method

Miyasaka, Satoshi*; Unome, Sota*; Tamura, Ayako*; Ito, Yoshiaki*; Ishizaki, Azusa; Sanada, Yukihisa

Nihon Rimoto Senshingu Gakkai Dai-63-Kai (Heisei-29-Nendo Shuki) Gakujutsu Koenkai Rombunshu (CD-ROM), p.81 - 84, 2017/11

Information of snow depth is important to improve the airborne radiation measurement in the winter. The snow depth is enable to estimate by the aerial photograph which is obtained at the same time with the radiation measurement before and after the snowfall. We attempted optimization parameters which used to make a Digital Surface Model (DSM) using Structure from Motion (SfM) method for estimation of the snow depth. As a result, to enable to measure precisely the snow depth was indicated. However, the estimated snow depth in the forest area was relatively not so accurate because fallen leaves and a tree move were prevented to measure DSM precisely.