000264991 001__ 264991
000264991 005__ 20190812204800.0
000264991 037__ $$aCONF
000264991 245__ $$aAn Algorithm for Odor Source Localization based on Source Term Estimation
000264991 260__ $$c2019
000264991 269__ $$a2019
000264991 300__ $$a6
000264991 336__ $$aConference Papers
000264991 520__ $$aFinding sources of airborne chemicals with mobile sensing systems finds applications across the security, safety, domestic, medical, and environmental domains. In this paper, we present an algorithm based on source term estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose an innovative strategy to balance exploration and exploitation in navigation. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments.
000264991 700__ $$g256890$$aRahbar, Faezeh$$0249512
000264991 700__ $$g232809$$aMarjovi, Ali$$0253947
000264991 700__ $$0241071$$aMartinoli, Alcherio$$g105782
000264991 7112_ $$aIEEE International Conference on Robotics and Automation 2019$$cMontreal, Canada$$dMay 20-24, 2019
000264991 8560_ $$ffaezeh.rahbar@epfl.ch
000264991 909C0 $$pDISAL$$malcherio.martinoli@epfl.ch$$0252151$$zCharbonnier, Valérie$$xU11904
000264991 909CO $$pconf$$pENAC$$ooai:infoscience.epfl.ch:264991
000264991 960__ $$afaezeh.rahbar@epfl.ch
000264991 961__ $$afantin.reichler@epfl.ch
000264991 973__ $$aEPFL$$rREVIEWED
000264991 980__ $$aCONF
000264991 981__ $$aoverwrite