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Work Report in Robotics Team at CERN since 1 st of May of 2017

Learn about the use of robots in the Robotics Team at CERN for the inspection and maintenance of radioactive and hazardous objects in challenging environments. Discover the development of the CERNRoboticFramework and its applications in object recognition, filtering box, TIM alignment, and WaveCatcher experiment.

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Work Report in Robotics Team at CERN since 1 st of May of 2017

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  1. Work Report in Robotics Team at CERNsince 1st of May of 2017 Jorge Camarero Vera

  2. Needs for robotic solutions @ CERN • Operation and maintenance of radioactive objects • Most of them are obsolete, without proper documentation and drawings, any intervention may lead to surprises • Risk of contamination • Inspection of hazardous and unstructured environments • In the past, people in EN/STI-EN/SMM went close to or above 2 mSv in a year • From an analysis of ALARA interventions, about 50% of the dose to personnel originated from very simple actions, such as visual inspections • We decided therefore to use robots for those dose-costly operations

  3. Robots • Telemax • Teodor • CERNBot

  4. CERNRoboticFramework • It allows control the CERNBot and their sensors. • It has been developed in C++

  5. Object Recognition using Deep Learning • Detection of different objects using Faster R-CNN1 deep neural network with Resnet101 • Using a Intel RealSense RGBD Camera to extract the point cloud inside of the Bounding Box. On this way we can get the position of the object with respect to the camera. • Ren, S., He, K., Girshick, R., & Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99), (2015).

  6. Object Recognition using Deep Learning Filtering Box • Using segmentation and clustering algorithms to find features in the point cloud. Using these features its possible to align a CAD model and to get the 6D position of the object with respect to the camera.

  7. RealSense Camera

  8. TIM alignment using Deep Learning • TIM, Train Inspection Monorail, is a mini vehicle autonomously monitoring the 27-km long LHC tunnel and moving along tracks suspended from the tunnel's ceiling. • Using object detection algorithms to align TIM with objects of interest. This is necessary to do precision measurements.

  9. WaveCatcher – UA9 Experiment • The UA9 experiment is investigating how crystals could help to steer particle beams in high-energy colliders. • WaveCatcher is a powerful and low cost digitizer built in Laboratoire de L’Accélérateur Linéaire. Their number of channels currently ranges between 2 and 64 (+8) channels. • They all make use of the SAMLONG analog memory chips which permit sampling the input signal between 400 MS/s and 3.2 GS/s over 12 bits and with a signal bandwidth of 500 MHz.

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