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Research Papers

Probabilistic-Based Robotic Radiation Mapping Using Sparse Data

[+] Author and Article Information
Robin McDougall

Mechatronic and Robotic Systems Laboratory,
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: robin.mcdougall@astrazeneca.com

Scott B. Nokleby

Mechatronic and Robotic Systems Laboratory,
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: scott.nokleby@uoit.ca

Ed Waller

Faculty of Energy Systems and Nuclear Science,
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: ed.waller@uoit.ca

1Corresponding author.

Manuscript received May 18, 2017; final manuscript received September 22, 2017; published online March 5, 2018. Assoc. Editor: Michal Kostal.

ASME J of Nuclear Rad Sci 4(2), 021009 (Mar 05, 2018) (10 pages) Paper No: NERS-17-1054; doi: 10.1115/1.4038185 History: Received May 18, 2017; Revised September 22, 2017

This paper presents a novel methodology for generating radiation intensity maps using a mobile robotic platform and an integrated radiation model. The radiation intensity mapping approach consists of two stages. First, radiation intensity samples are collected using a radiation sensor mounted on a mobile robotic platform, reducing the risk of exposure to humans from an unknown radiation field. Next, these samples, which need only to be taken from a subsection of the entire area being mapped, are then used to calibrate a radiation model of the area. This model is then used to predict the radiation intensity field throughout the rest of the area that could not be directly measured. The performance of the approach is evaluated through experiments. The results show that the developed system is effective at achieving the goal of generating radiation maps using sparse data.

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Figures

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Fig. 1

Overview of the mapping algorithm

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Fig. 2

Robotic radiation mapping platform—Radbot

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Fig. 4

Radiation sources and test stand

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Fig. 3

Overview of the test lab

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Fig. 5

The control center—map in progress

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Fig. 6

Typical physical layout map viewed at the operator station

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Fig. 7

Two source locations (trefoil symbols) within the lab

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Fig. 12

Predicted versus observed radiation intensities for two sources scenario

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Fig. 13

Probabilistic radiation map for two sources scenario

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Fig. 14

Three source locations (trefoil symbols) within the lab

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Fig. 15

Sample locations for three sources scenario

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Fig. 8

Sample locations for two sources scenario

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Fig. 9

Inferred source locations (crosses) for two sources scenario

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Fig. 10

Probabilistic radiation map for two sources scenario

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Fig. 11

Additional locations sampled for validation of map for two sources scenario

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Fig. 16

Inferred source locations (crosses) for three sources scenario

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Fig. 17

Probabilistic radiation map for three sources scenario

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Fig. 18

Additional locations sampled for validation of map for three sources scenario

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Fig. 19

Predicted versus observed radiation intensities—three sources scenario

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