2020 Fourth IEEE International Conference on Robotic Computing (IRC) 2020

DOI: 10.1109/irc.2020.00024

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Informative Mobile Robot Exploration for Radiation Source Localization with a Particle Filter

Nantawat Pinkam

¹

,

Armağan Elibol

²

,

Nak Young Chong

³

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Cited by 5 publications

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“…The prior pdf p(x k |z 1:k−1 ) is obtained from the system model (1) and the pdf p(x k−1 |z 1:k−1 ) from the previous (k − 1) update step (4).…”

Section: Particle Filtermentioning

confidence: 99%

A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World

2023

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected Papers.

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In this paper, we are focusing on comparing solutions for localizing an unknown radiation source in both a Gazebo simulator and the real world. A proper simulation of the environment, sensors, and radiation source can significantly reduce the development time of robotic algorithms. We proposed a simple sampling importance resampling (SIR) particle filter. To verify its effectiveness and similarities, we first tested the algorithm's performance in the real world and then in the Gazebo simulator. In experiment, we used a 2-inch NaI(Tl) radiation detector and radiation source Cesium 137 with an activity of 330 Mbq. We compared the algorithm process using the evolution of information entropy, variance, and Kullback-Leibler divergence. The proposed metrics demonstrated the similarity between the simulator and the real world, providing valuable insights to improve and facilitate further development of radiation search and mapping algorithms.

“…The prior pdf p(x k |z 1:k−1 ) is obtained from the system model (1) and the pdf p(x k−1 |z 1:k−1 ) from the previous (k − 1) update step (4).…”

Section: Particle Filtermentioning

confidence: 99%

A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World

2023

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected Papers.

View full text Add to dashboard Cite

In this paper, we are focusing on comparing solutions for localizing an unknown radiation source in both a Gazebo simulator and the real world. A proper simulation of the environment, sensors, and radiation source can significantly reduce the development time of robotic algorithms. We proposed a simple sampling importance resampling (SIR) particle filter. To verify its effectiveness and similarities, we first tested the algorithm's performance in the real world and then in the Gazebo simulator. In experiment, we used a 2-inch NaI(Tl) radiation detector and radiation source Cesium 137 with an activity of 330 Mbq. We compared the algorithm process using the evolution of information entropy, variance, and Kullback-Leibler divergence. The proposed metrics demonstrated the similarity between the simulator and the real world, providing valuable insights to improve and facilitate further development of radiation search and mapping algorithms.

Particle Filter Method Based on Angle of Arrival for Seeking Radioactive Source

¹

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²

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et al. 2023

Lecture Notes in Electrical Engineering

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No abstract

Localizing multiple radiation sources actively with a particle filter

Lazna,

Zalud

2024

Nuclear Engineering and Technology

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No abstract

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