A Reinforcement Learning (RL)-Based Hybrid Search Method for Hidden Object Discovery using GPR

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dc.contributor.authorAlpdemir, Mahmut Nedim
dc.contributor.authorSezgin, Mehmet
dc.date.accessioned2023-11-10T09:13:29Z
dc.date.available2023-11-10T09:13:29Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractGround Penetrating Radar (GPR) systems are effective tools for discovering various types of buried objects (such as military mines, metal objects, pieces of underground infrastructures etc.). One challenge is to find an efficient search procedure that would reduce the operation time and optimize resource utilization, such as minimizing the power consumption for battery operated UAVs or fuel consumption of land vehicles. Current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper we report on a Reinforcement Learning (RL) based search method for training the guidance logic of possible platforms hosting the GPR, so that they navigate more efficiently. We illustrate the applicability of the method using a high-fidelity simulation environment and a newly implemented RL framework. Initial results suggests that combination of a coarse navigation scheme and an RL based training procedure based on GPR scan patterns, can lead to a more efficient target discovery procedure for host platforms.en_US
dc.identifier.doi10.1109/IC_ASET58101.2023.10150516en_US
dc.identifier.scopus2-s2.0-85164270367en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6985
dc.identifier.urihttps://doi.org/10.1109/IC_ASET58101.2023.10150516
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectCoverage path planning, ground penetrating radar, machine learning, reinforcement learningen_US
dc.titleA Reinforcement Learning (RL)-Based Hybrid Search Method for Hidden Object Discovery using GPRen_US
dc.typeConference Objecten_US

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