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Extreme learning machine terrain-based navigation for unmanned aerial vehicles

التفاصيل البيبلوغرافية
العنوان: Extreme learning machine terrain-based navigation for unmanned aerial vehicles
المؤلفون: Ah-Hwee Tan, Meng-Hiot Lim, Ee May Kan, Swee Ping Yeo, Yew-Soon Ong
المصدر: Neural Computing and Applications. 22:469-477
بيانات النشر: Springer Science and Business Media LLC, 2012.
سنة النشر: 2012
مصطلحات موضوعية: Computer science, business.industry, Real-time computing, Elevation, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Terrain, Artificial Intelligence, Path (graph theory), Global Positioning System, Computer vision, Artificial intelligence, business, Scale (map), Software, Extreme learning machine
الوصف: Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the on-line implementation by supporting multi-resolution terrain access, thus capable of generating an immediate path with high accuracy within the allowable time scale. Numerical tests have demonstrated the potential benefits of the approach.
تدمد: 1433-3058
0941-0643
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::b9d38105799090a5065ac7ce746b5597
https://doi.org/10.1007/s00521-012-0866-9
حقوق: OPEN
رقم الأكسشن: edsair.doi...........b9d38105799090a5065ac7ce746b5597
قاعدة البيانات: OpenAIRE
الوصف
تدمد:14333058
09410643