Generating dynamically feasible trajectories for quadrotor cameras
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Bibliographic record
Abstract
When designing trajectories for quadrotor cameras, it is important that the trajectories respect the dynamics and physical limits of quadrotor hardware. We refer to such trajectories as being feasible . In this paper, we introduce a fast and user-friendly algorithm for generating feasible quadrotor camera trajectories. Our algorithm takes as input an infeasible trajectory designed by a user, and produces as output a feasible trajectory that is as similar as possible to the user's input. By design, our algorithm does not change the spatial layout or visual contents of the input trajectory. Instead, our algorithm guarantees the feasibility of the output trajectory by re-timing the input trajectory, perturbing its timing as little as possible while remaining within velocity and control force limits. Our choice to perturb the timing of a shot, while leaving the spatial layout and visual contents of the shot intact, leads to a well-behaved non-convex optimization problem that can be solved at interactive rates. We implement our algorithm in an open-source tool for designing quadrotor camera shots, where we achieve interactive performance across a wide range of camera trajectories. We demonstrate that our algorithm is between 25x and 45x faster than a spacetime constraints approach implemented using a commercially available solver. As we scale to more finely discretized trajectories, this performance gap widens, with our algorithm outperforming spacetime constraints by between 90x and 180x. Finally, we fly 5 feasible trajectories generated by our algorithm on a real quadrotor camera, producing video footage that is faithful to Google Earth shot previews, even when the trajectories are at the quadrotor's physical limits.
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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 | 0.000 | 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