A Reinforcement Learning (RL)-Based Hybrid Search Method for Hidden Object Discovery using GPR
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Ground 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.
Açıklama
Anahtar Kelimeler
Coverage path planning, ground penetrating radar, machine learning, reinforcement learning
Kaynak
Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023
WoS Q DeÄŸeri
Scopus Q DeÄŸeri
N/A
