TY - GEN
T1 - Scaled experiments in vision-based approach and landing in high sea states
AU - Nicholson, Duncan J.
AU - Hendrick, Christopher M.
AU - Jaques, Emma R.
AU - Horn, Joseph Francis
AU - Langelaan, Jack W.
AU - Sydney, Anish J.
N1 - Funding Information:
This work was sponsored by the Office of Naval Research (ONR) under grant N00014-20-1-2092. The views and conclusions contained herein are those of the authors only and should not be interpreted as representing those of ONR, the U.S. Navy, or the U.S. Government. The authors also thank Jared Soltis for his assistance in conducting flight tests.
Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Landings on ship decks in higher sea states pose significant challenges to UAV operators. This paper describes the development and testing of a self-contained vision-based autonomous deck landing system. A single monocular smart camera was used for detection and 6DOF pose estimation of a recursive AprilTag marker array. The fiducial marker array was detected and localized at 48 Hz from distances up to 5 m. The scalable fiducial marker system and wide field of view camera used were found to improve deck observability and the quality of deck state estimates over a wider range of distances compared to non-scalable visual aids. Fusion of vision and inertial sensor data was performed using an Unscented Kalman Filter for relative deck state estimation. Tau trajectories were generated and followed using an explicit model following controller created from identified vehicle dynamic models. Performance of the vision system and estimator was measured using two separate motion capture systems for ground truth in hovering and landing flight tests. Fifteen successful autonomous landings were performed on the model ship deck in scaled sea states as high as six.
AB - Landings on ship decks in higher sea states pose significant challenges to UAV operators. This paper describes the development and testing of a self-contained vision-based autonomous deck landing system. A single monocular smart camera was used for detection and 6DOF pose estimation of a recursive AprilTag marker array. The fiducial marker array was detected and localized at 48 Hz from distances up to 5 m. The scalable fiducial marker system and wide field of view camera used were found to improve deck observability and the quality of deck state estimates over a wider range of distances compared to non-scalable visual aids. Fusion of vision and inertial sensor data was performed using an Unscented Kalman Filter for relative deck state estimation. Tau trajectories were generated and followed using an explicit model following controller created from identified vehicle dynamic models. Performance of the vision system and estimator was measured using two separate motion capture systems for ground truth in hovering and landing flight tests. Fifteen successful autonomous landings were performed on the model ship deck in scaled sea states as high as six.
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U2 - 10.2514/6.2022-3279
DO - 10.2514/6.2022-3279
M3 - Conference contribution
AN - SCOPUS:85135383538
SN - 9781624106354
T3 - AIAA AVIATION 2022 Forum
BT - AIAA AVIATION 2022 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA AVIATION 2022 Forum
Y2 - 27 June 2022 through 1 July 2022
ER -
