Detail publikace

Optimizing the Localization of Gamma Radiation Point Sources using a UGV

LÁZNA, T.

Originální název

Optimizing the Localization of Gamma Radiation Point Sources using a UGV

Anglický název

Optimizing the Localization of Gamma Radiation Point Sources using a UGV

Jazyk

en

Originální abstrakt

The paper is concerned with the autonomous robotic measurement of gamma radiation and the localization of its sources. The objective is to find techniques that allow faster localization compared to mapping along parallel lines. To achieve this, an unmanned ground vehicle (UGV) is utilized; the device is equipped with a sensitive dual-probe radiation detection system and a module for precise self-localization and navigation. The suggested solution involves exploring the region of interest along circular trajectories and estimating the sources' parameters, the coordinates in particular. The estimation accuracy is then increased using the Gauss-Newton method. The approach was verified through experiments in real conditions; the achieved result consists in a threefold decrease of the time requirement while the localization accuracy remains comparable to that acquired by the radiation mapping. The benefits of the presented research comprise the qualitative expansion of the radiation protection domain and also the applicability of several utilized algorithms in other fields or activities, including, for example, the autonomous robotic inspection of industrial facilities.

Anglický abstrakt

The paper is concerned with the autonomous robotic measurement of gamma radiation and the localization of its sources. The objective is to find techniques that allow faster localization compared to mapping along parallel lines. To achieve this, an unmanned ground vehicle (UGV) is utilized; the device is equipped with a sensitive dual-probe radiation detection system and a module for precise self-localization and navigation. The suggested solution involves exploring the region of interest along circular trajectories and estimating the sources' parameters, the coordinates in particular. The estimation accuracy is then increased using the Gauss-Newton method. The approach was verified through experiments in real conditions; the achieved result consists in a threefold decrease of the time requirement while the localization accuracy remains comparable to that acquired by the radiation mapping. The benefits of the presented research comprise the qualitative expansion of the radiation protection domain and also the applicability of several utilized algorithms in other fields or activities, including, for example, the autonomous robotic inspection of industrial facilities.

Dokumenty

BibTex


@inproceedings{BUT148156,
  author="Tomáš {Lázna}",
  title="Optimizing the Localization of Gamma Radiation Point Sources using a UGV",
  annote="The paper is concerned with the autonomous robotic measurement of gamma radiation and the localization of its sources. The objective is to find techniques that allow faster localization compared to mapping along parallel lines. To achieve this, an unmanned ground vehicle (UGV) is utilized; the device is equipped with a sensitive dual-probe radiation detection system and a module for precise self-localization and navigation. The suggested solution involves exploring the region of interest along circular trajectories and estimating the sources' parameters, the coordinates in particular. The estimation accuracy is then increased using the Gauss-Newton method. The approach was verified through experiments in real conditions; the achieved result consists in a threefold decrease of the time requirement while the localization accuracy remains comparable to that acquired by the radiation mapping. The benefits of the presented research comprise the qualitative expansion of the radiation protection domain and also the applicability of several utilized algorithms in other fields or activities, including, for example, the autonomous robotic inspection of industrial facilities.",
  booktitle="2018 ELEKTRO",
  chapter="148156",
  doi="10.1109/ELEKTRO.2018.8398368",
  howpublished="electronic, physical medium",
  year="2018",
  month="may",
  pages="1--6",
  type="conference paper"
}