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dc.contributor.authorMalakar, Nabin K.en_US
dc.contributor.authorGladkov, Daniilen_US
dc.contributor.authorKnuth, Kevin H.en_US
dc.identifier.citationMalakar, Nabin K., Daniil Gladkov, and Kevin H. Knuth. 2013. "Modeling a sensor to improve its efficacy." Journal of Sensors 2013(481054): 1-11.en_US
dc.description.abstractRobots 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.en_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCC BY 3.0 (Attribution)en_US
dc.rights©2013 The Authorsen_US
dc.sourceJournal of Sensors
dc.subjectOptical detectorsen_US
dc.subjectMachine learningen_US
dc.subjectBayesian statistical decision theoryen_US
dc.titleModeling a Sensor to Improve its Efficacyen_US

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