0435994 20240103205146.020141204235959.9 000327200403074 10.1109/CDC.2012.6426702 6 s. C cav_un_epca*0436853978-1-4673-2066-50191-22163098-3103New YorkIEEE Computer Society2012 navigation dual control active learning cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0228606 Hofman Radek Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/AS/smidl-0435994.pdf VG20102013018 GA MV cav_un_auth*0265869 CEZ:AV0Z10750506 Navigation of unmanned aerial vehicles (UAVs) is a popular and well studied topic. In this contribution, we apply the existing decision theory to the problem of tracking atmospheric pollution, specifically accidental release of radiation. The UAVs work in tandem with the stationary radiation monitoring network to estimate consequences of the radiation accident. Results of this estimation serve as supporting material for the human decision makers, whose main concern is protection of the population. We test navigation of the UAVs with respect to two loss functions: mutual information and expected misclassification of affected people. Several approximations were designed to navigate the UAVs in real time. The simulation experiments confirm that UAVs have great potential to complement limited capabilities of the stationary radiation monitoring network. cav_un_auth*0311114 ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) 2012 /51./ Maui, Hawai 10.12.2012-13.12.2012 US 2015 BC 2 PR http://hdl.handle.net/11104/0240492 S 2012 000327200403074 WOS cav_un_epca*0436853 Proceedings of the 51st IEEE Annual Conference on Decision and Control (CDC) 978-1-4673-2066-5 0191-2216 3098 3103 New York IEEE Computer Society 2012