Open Collections

Tavasoli, Sina

University of British Columbia

This dissertation proposes the use of intelligent micro aerial vehicle (MAV) systems for autonomous indoor navigation, damage assessment, and survivor detection. The dissertation includes: 1) the development of specialized MAVs equipped with a range of sensors for different post-disaster scenarios; 2) the development of autonomous navigation and data collection algorithms for confined indoor environments; 3) the introduction and integration of autonomous path planning for comprehensive multi-view data collection; 4) the integration of advanced sensors for real-time autonomous navigation and mapping; and 5) proposal of a new strategy for autonomous survivor localization through thermal imaging. The navigation algorithms, sensor selection, board design, and the choice of the MAVs are all tailored to make the entire system compact and economical. To demonstrate the effectiveness of the proposed system, the autonomous algorithms and the specialized MAVs are validated numerically under different variations in floor plans and take-off locations. Finally, laboratory implementations in simulated post-disaster environments have been conducted to examine the robustness of the system. This study demonstrates that economical MAVs with specially selected sensors can accurately map the layout of the environment, localize the damage, and identify the survivors.

Text

2025-06-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/91264

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/