Browsing by Author "Cheng, Hsiu-Wen"

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Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area
(2019-11-01)
Cheng, Hsiu-Wen
;
Chen, Tsung-Lin
;
Tien, Chung-Hao
;
機械工程學系
;
光電工程學系
We proposed a vision-based methodology as an aid for an unmanned aerial vehicle (UAV) landing on a previously unsurveyed area. When the UAV was commanded to perform a landing mission in an unknown airfield, the learning procedure was activated to extract the surface features for learning the obstacle appearance. After the learning process, while hovering the UAV above the potential landing spot, the vision system would be able to predict the roughness value for confidence in a safe landing. Finally, using hybrid optical flow technology for motion estimation, we successfully carried out the UAV landing without a predefined target. Our work combines a well-equipped flight control system with the proposed vision system to yield more practical versatility for UAV applications. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
Motion Estimation by Hybrid Optical Flow Technology for UAV Landing in an Unvisited Area
(2019-03-20)
Cheng, Hsiu-Wen
;
Chen, Tsung-Lin
;
Tien, Chung-Hao
;
機械工程學系
;
光電工程學系
The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.