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Reinforcement Learning-based Collision Avoidance for UAV
dc.creator | Jevtić, Đorđe | |
dc.creator | Miljković, Zoran | |
dc.creator | Petrović, Milica | |
dc.creator | Jokić, Aleksandar | |
dc.date.accessioned | 2023-07-07T07:53:38Z | |
dc.date.available | 2023-07-07T07:53:38Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-86-7466-970-9 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/6896 | |
dc.description.abstract | One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles. | sr |
dc.language.iso | en | sr |
dc.publisher | ETRAN Society, The Society for Electronics, Telecommunications, Computing, Automatics and Nuclear engineering supported by IEEE | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200105/RS// | sr |
dc.rights | openAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023) | sr |
dc.subject | Unmanned Aerial Vehicles (UAVs) | sr |
dc.subject | Collision avoidance | sr |
dc.subject | Reinforcement learning | sr |
dc.subject | Q-learning | sr |
dc.subject | Simulation | sr |
dc.subject | MATLAB Simulink environment | sr |
dc.subject | Autonomous localization and navigation | sr |
dc.subject | Markov decision process | sr |
dc.subject | The flying mobile robot | sr |
dc.subject | The sequential decision-making model | sr |
dc.title | Reinforcement Learning-based Collision Avoidance for UAV | sr |
dc.type | conferenceObject | sr |
dc.rights.license | BY | sr |
dc.rights.holder | Prof. Zoran Miljković | sr |
dc.citation.epage | 6 | |
dc.citation.issue | 5496 | |
dc.citation.rank | M33 | |
dc.citation.spage | 1 | |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/17352/bitstream_17352.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_machinery_6896 | |
dc.type.version | publishedVersion | sr |