Modeling a Sensor to Improve its Efficacy

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Modeling a Sensor to Improve its Efficacy

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title Modeling a Sensor to Improve its Efficacy
contributor.author Malakar, Nabin K.
contributor.author Gladkov, Daniil
contributor.author Knuth, Kevin H.
description.abstract Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a spatially distributed region within its field of view weighted by its spatial sensitivity function (SSF). We demonstrate that by incorporating an accurate model of the light sensor SSF into the likelihood function of a Bayesian inference engine, an autonomous system can make improved inferences about its surroundings. The method presented here is data based, fairly general, and made with plug-and-play in mind so that it could be implemented in similar problems.
identifier.citation Malakar, Nabin K., Daniil Gladkov, and Kevin H. Knuth. 2013. "Modeling a sensor to improve its efficacy." Journal of Sensors 2013(481054): 1-11.
identifier.issn 1687-725X
identifier.uri http://hdl.handle.net/10735.1/3869
identifier.startpage 481054
subject Optical detectors
subject Machine learning
subject Robots
subject Bayesian statistical decision theory
date.created 2013-05-20
date.issued 2013-05-20
publisher Hindawi Publishing Corporation
relation.uri http://dx.doi.org/10.1155/2013/481054
rights CC BY 3.0 (Attribution)
rights ©2013 The Authors
rights.uri http://creativecommons.org/licenses/by/3.0/
language.iso en
source Journal of Sensors

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CC BY 3.0 (Attribution) Except where otherwise noted, this item's license is described as CC BY 3.0 (Attribution)