Extreme learning machine terrain-based navigation for unmanned aerial vehicles

• Main Authors: KAN, Ee May, LIM, Meng Hiot, ONG, Yew Soon, TAN, Ah-hwee, YEO, Swee Ping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Unmanned aerial vehicles (UAVs); Extreme learning machines (ELM); Terrain-based navigation; Computer and Systems Architecture; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5193; https://ink.library.smu.edu.sg/context/sis_research/article/6196/viewcontent/ELM_UAV.pdf
• Institution: Singapore Management University

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.