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research-article

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, Canada
robin.mcdougall@astrazeneca.com

Scott B. Nokleby

Mechatronic and Robotic Systems Laboratory University of Ontario Institute of Technology Oshawa, Canada
scott.nokleby@uoit.ca

Ed Waller

Mechatronic and Robotic Systems Laboratory University of Ontario Institute of Technology Oshawa, Canada
ed.waller@uoit.ca

1Corresponding author.

ASME doi:10.1115/1.4038185 History: Received May 18, 2017; Revised September 22, 2017

Abstract

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.

Copyright (c) 2017 by ASME
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