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http://hdl.handle.net/1942/42846
Title: | Learning Multiple Radiation SourceDistribution Models using Gaussian Processes | Authors: | DE SCHEPPER, David SIMONS, Mattias SCHROEYERS, Wouter KELLENS, Karel DEMEESTER, Eric |
Issue Date: | 2023 | Source: | 56th International Symposium on Robotics (ISR Europe), Stuttgart (Germany), 26/09/2023-27/09/2023 | Abstract: | Over the past years, automated, robotic radiation source localisation has become of emerging interest due to a variety of reasons, e.g. disaster response, homeland security, or dismantling and decommissioning of nuclear contaminated areas. Nowadays, to perform in-the-field measurements, radiation protection officers and safety personnel are tasked with characterising an environment before a nuclear contaminated area can enter the final phase of the dismantling and decommissioning process. This involves some severe drawbacks such as the absence of any a priori information on the potentially contaminated area. Besides the potential health risks involved, this preliminary task is very time-consuming and prone to errors concerning the taken measurements and the post-processing of the obtained measurements. To further automate this task, this paper presents an approach to build a radiation model of the environment based on measurements collected by a robotic arm during in-situ laboratory tests. The task of estimating the radiation distribution in an environment is modeled as a regression problem, where the framework of Gaussian Processes is adopted. The experiments conducted in an in-situ laboratory environment demonstrate that the approach is feasible to model the radiation distribution caused by multiple radiation point sources, for both static measurements, where a robot stops moving to sample a measurement, and dynamic measurements, where a robot executes measurements in a continuous manner. | Keywords: | Index Terms-Robotics for nuclear power plants;Ra- diological mapping;Gaussian Processes;Field robotics;Robotic exploration | Document URI: | http://hdl.handle.net/1942/42846 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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a77-de_schepper final.pdf | Conference material | 3.91 MB | Adobe PDF | View/Open |
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