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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17024

Title: Informative Mobile Robot Exploration for Radiation Source Localization with a Particle Filter
Authors: Pinkam, Nantawat
Elibol, Armagan
Chong, Nak Young
Keywords: source localization
information theory
mobile robot
Issue Date: 2020-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Start page: 107
End page: 112
DOI: 10.1109/IRC.2020.00024
Abstract: In this work, we consider the localization problem of an unknown radiation source with measurement uncertainty by using robotic systems in a geometric environment. We proposed the scheme for localization of a radioactive source using the particle filter with information gain-based exploration. The traditional method to localize the radiation is to use the gradient descent algorithm. However, such the algorithm may fail to work in the case of uncertain measurements, which lead to an inaccurate outcome. On the other hand, a standard particle filter can be used to deal with the measurement uncertainty, but the estimated intensity result may be unstable since it only uses the current measurement update as a likelihood function. To solve the problem of measurement uncertainty and unstable intensity result, we proposed an exploration method using the information gain with particle filter. The algorithm takes the information of the particles in the filter to estimate the possible actions for the robot. The expected information gain from those actions can be used to select the best possible action for the robot. The proposed method has been verified by the simulations. The proposed strategy can decrease the time it takes to finish the task comparing to the conventional methods such as the lawn mowing algorithm and source estimation seeking algorithm.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020, pp.107-112. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/17024
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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