Scaled Experiments in Vision-Based Approach and Landing in High Sea States
Open Access
Author:
Nicholson, Duncan
Graduate Program:
Aerospace Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 13, 2022
Committee Members:
Jacob Willem Langelaan, Thesis Advisor/Co-Advisor Joseph Francis Horn, Committee Member Amy Pritchett, Program Head/Chair Eric Norman Johnson, Committee Member
Keywords:
Ship Deck Landing UAV UAS Fiducial Control Estimation Precision Landing shipboard sUAS
Abstract:
Landings on moving, rotating ship decks pose significant challenges to UAV operators. In this thesis, a vision-based autonomous deck landing system was developed and tested with scale models in a series of wave conditions. 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.
