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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April 2018
Research-Article
Probabilistic-Based Robotic Radiation Mapping Using Sparse Data
Robin McDougall,
Robin McDougall
Mechatronic and Robotic Systems Laboratory,
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: robin.mcdougall@astrazeneca.com
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: robin.mcdougall@astrazeneca.com
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Scott B. Nokleby,
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
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: scott.nokleby@uoit.ca
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Ed Waller
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
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: ed.waller@uoit.ca
Search for other works by this author on:
Robin McDougall
Mechatronic and Robotic Systems Laboratory,
University of Ontario Institute of Technology,
Oshawa, ON L1H 7K4, Canada
e-mail: robin.mcdougall@astrazeneca.com
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
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
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. Apr 2018, 4(2): 021009 (10 pages)
Published Online: March 5, 2018
Article history
Received:
May 18, 2017
Revised:
September 22, 2017
Citation
McDougall, R., Nokleby, S. B., and Waller, E. (March 5, 2018). "Probabilistic-Based Robotic Radiation Mapping Using Sparse Data." ASME. ASME J of Nuclear Rad Sci. April 2018; 4(2): 021009. https://doi.org/10.1115/1.4038185
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