Localization of cask and plug remote handling system in ITER using multiple video cameras

Ferreira, João Filipe; Vale, Alberto; Ribeiro, M. Isabel

doi:10.1016/j.fusengdes.2012.10.008

Cited by 7 publications

(2 citation statements)

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“…Lionel et al ( 2018 ) introduced the virtual reality technology in the assembly and tooling design of the tokamak diverter to assist teleoperators and successfully achieved assembly with a gap of <1 mm. Ferreira et al ( 2012 ) designed a localization system based on cameras to accurately estimate the position and direction of CPRH by capturing video streams for the implementation of an augmented reality system. Liu et al ( 2020 ) proposed the vision-based breakpoint detection algorithm and successfully identified and captured tiles that had fallen onto the diverter by employing the watershed segmentation algorithm.…”

Section: Introductionmentioning

confidence: 99%

An improved model for target detection and pose estimation of a teleoperation power manipulator

et al. 2023

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IntroductionA hot cell is generally deployed with a teleoperation power manipulator to complete tests, operations, and maintenance. The position and pose of the manipulator are mostly acquired through radiation-resistant video cameras arranged in the hot cell. In this paper, deep learning-based target detection technology is used to establish an experimental platform to test the methods for target detection and pose estimation of teleoperation power manipulators using two cameras.MethodsIn view of the fact that a complex environment affects the precision of manipulator pose estimation, the dilated-fully convolutional one-stage object detection (dilated-FCOS) teleoperation power manipulator target detection algorithm is proposed based on the scale of the teleoperation power manipulator. Model pruning is used to improve the real-time performance of the dilated-FCOS teleoperation power manipulator target detection model. To improve the detection speed for the key points of the teleoperation power manipulator, the keypoint detection precision and model inference speed of different lightweight backbone networks were tested based on the SimpleBaseline algorithm. MobileNetv1 was selected as the backbone network to perform channel compression and pose distillation on the upsampling module so as to further optimize the inference speed of the model.Results and discussionCompared with the original model, the proposed model was experimentally proven to reach basically the same precision within a shorter inference time (only 58% of that of the original model). The experimental results show that the compressed model basically retains the precision of the original model and that its inference time is 48% of that of the original model.

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Section: Introductionmentioning

confidence: 99%

An improved model for target detection and pose estimation of a teleoperation power manipulator

et al. 2023

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“…During the maintenance operations of transportation, the pose (position and orientation) of each vehicle must be continuously evaluated using sensors data. Although the first studies of localization of the CTS in ITER have been accomplished, as described in [17], in this paper it is assumed that the pose of the vehicle is always known without any uncertainty.…”

Section: Introductionmentioning

confidence: 99%