Document Type
Article
Publication Date
Spring 5-20-2013
DOI
10.1155/2013/481054
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.
Recommended Citation
Malakar, Nabin K.; Gladkov, Daniil; and Knuth, Kevin H., "Modeling a Sensor to Improve its Efficacy" (2013). Physics Faculty Scholarship. 7.
https://scholarsarchive.library.albany.edu/physics_fac_scholar/7
Included in
Artificial Intelligence and Robotics Commons, Engineering Physics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Robotics Commons, Statistical Models Commons
Terms of Use
This work is made available under the Scholars Archive Terms of Use.
Comments
Publisher Acknowledgment
This is the Publisher’s PDF of the following article made available by Hindawi © 2013
Malakar N.K., Gladkov D., and Knuth K.H. 2013. Modeling a sensor to improve its efficacy, Journal of Sensors, vol. 2013, Article ID 481054, 11 pages, 2013. doi:10.1155/2013/481054, arXiv:1303.4385 [physics.ins-det]