Quadrotor circumnavigation of an unknown moving target using camera vision‐based measurements
Why this work is in the frame
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Bibliographic record
Abstract
This study proposes a vision‐based motion estimation and target tracking algorithm for a quadrotor unmanned aerial vehicle circumnavigation around a moving mobile target whose velocity is unknown and time varying. In this study, the authors assume that the quadrotor is equipped with onboard downward‐looking camera, as a means to determine position of the quadrotor relative to the target. The proposed circumnavigation control algorithm relies essentially on two distinct phases, namely the virtual target tracking and the circumnavigation phase. To prepare for these phases, a predefined sphere, with a desired radius having the moving target position as its centre, is constructed along with a virtual target point that can move on its surface. During the whole tracking procedure, the quadrotor is first commanded to reach the virtual target point located at the projection of the ground vehicle's position onto the surface area of the sphere. When the quadrotor's position and velocity approaches the virtual target point with a given accuracy, the second phase is initiated to provide the quadrotor with more precise guidance to start orbiting at a specific height from level ground around the moving target. In this manner, the virtual target point is given the ability to manoeuvre itself in a circular motion above the moving target. The developed orbit manoeuvre uses an estimate of the moving target's velocity, obtained from a predictor scheme able to achieve velocity estimation as fast as possible.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it