Citation: Tomita, H.; Hara, S.; Mukai,
A.; Yamagishi, K.; Ebi, H.; Shimazoe,
K.; Tamura, Y.; Woo, H.; Takahashi,
H.; Asama, H.; et al. Path-Planning
System for Radioisotope
Identification Devices Using 4π
Gamma Imaging Based on Random
Forest Analysis. Sensors 2022, 22,
4325. https://doi.org/10.3390/
s22124325
Academic Editors: M. Jamal Deen,
Subhas Mukhopadhyay,
Yangquan Chen, Simone Morais,
Nunzio Cennamo and Junseop Lee
Received: 16 April 2022
Accepted: 1 June 2022
Published: 7 June 2022
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Article
Path-Planning System for Radioisotope Identification Devices
Using 4π Gamma Imaging Based on Random Forest Analysis
Hideki Tomita
1,
* , Shintaro Hara
1
, Atsushi Mukai
1
, Keita Yamagishi
1
, Hidetake Ebi
1
, Kenji Shimazoe
2
,
Yusuke Tamura
3
, Hanwool Woo
4
, Hiroyuki Takahashi
2
, Hajime Asama
5
, Fumihiko Ishida
6
, Eiji Takada
6
,
Jun Kawarabayashi
7
, Kosuke Tanabe
8
and Kei Kamada
9
1
Department of Energy Engineering, Nagoya University, Nagoya 464-8603, Japan;
hara.shintaro@e.mbox.nagoya-u.ac.jp (S.H.); mukai.atsushi.k5@s.mail.nagoya-u.ac.jp (A.M.);
yamagishi.keita@f.mbox.nagoya-u.ac.jp (K.Y.); ebi.hidetake.g5@s.mail.nagoya-u.ac.jp (H.E.)
2
Department of Nuclear Engineering and Management, The University of Tokyo, Tokyo 113-8656, Japan;
shimazoe@bioeng.t.u-tokyo.ac.jp (K.S.); leo@n.t.u-tokyo.ac.jp (H.T.)
3
Department of Robotics, Tohoku University, Sendai 980-8579, Japan; ytamura@tohoku.ac.jp
4
Department of Mechanical Systems Engineering, Kogakuin University, Hachioji 192-0015, Japan;
at13710@g.kogakuin.jp
5
Department of Precision Engineering, The University of Tokyo, Tokyo 113-8656, Japan;
asama@robot.t.u-tokyo.ac.jp
6
National Institute of Technology, Toyama College, Toyama-shi 939-8630, Japan; ishida-f@nc-toyama.ac.jp (F.I.);
takada@nc-toyama.ac.jp (E.T.)
7
Department of Nuclear Safety Engineering, Tokyo City University, Tokyo 158-8557, Japan; jkawara@tcu.ac.jp
8
National Research Institute of Police Science, Chiba 277-0882, Japan; tanabe@nrips.go.jp
9
Institute of Engineering Innovation, Tohoku University, Sendai 980-8579, Japan; kamada@imr.tohoku.ac.jp
* Correspondence: tomita@nagoya-u.jp; Tel.: +81-52-789-4695
Abstract:
We developed a path-planning system for radiation source identification devices using
4
π
gamma imaging. The estimated source location and activity were calculated by an integrated
simulation model by using 4
π
gamma images at multiple measurement positions. Using these calcu-
lated values, a prediction model to estimate the probability of identification at the next measurement
position was created by via random forest analysis. The path-planning system based on the prediction
model was verified by integrated simulation and experiment for a
137
Cs point source. The results
showed that
137
Cs point sources were identified using the few measurement positions suggested by
the path-planning system.
Keywords: path planning; radioisotope identification; 4π gamma imaging; random forest
1. Introduction
Radiation sources should be handled carefully and controlled strictly. However, in the
events of theft and loss of sources or undesired acts of terrorism using such sources, it is
necessary to identify multiple sources over a wide search area rapidly [
1
]. Several methods
for radiation source identification have been developed [
2
–
7
]. For example, Huo et al.
reported a method to estimate the location and intensity of radiation sources by using a
mobile robot equipped with a Geiger–Müller (GM) counter and laser range sensor [
4
]. They
investigated the selection of the measurement position by reinforcement learning for the
autonomous identification of the radiation sources. Besides this, methods to visualize the
gamma-ray intensity in an environmental three-dimensional map were developed. Vetter
et al. demonstrated radiation source detection using both simultaneous localization and
mapping (SLAM) based on a light detection and ranging (LiDAR) system and gamma-ray
images obtained using gamma imaging methods such as coded-aperture and Compton
imaging [
5
]. Sato et al. applied this method to visualize the gamma-ray intensity at the site
of Fukushima Daiichi Nuclear Power Plant [6,7].
Sensors 2022, 22, 4325. https://doi.org/10.3390/s22124325 https://www.mdpi.com/journal/sensors
