High-Speed Obstacle-Avoidance with Agile Fixed-Wing Aircraft
Bibliographic record
Abstract
Agile fixed-wing aircraft aim to bridge the gap between rotor-craft and conventional fixed-wing aircraft, with the capability of maneuverable and even hovering flight like a rotor-craft, and of efficient long distance flight like a conventional fixed-wing aircraft. Avoiding obstacles in unknown environments is a challenging task with these platforms, as they have complicated dynamics and a limited payload, and they fly at high speeds. In this work, we present an obstacle-avoidance strategy that avoids collisions while steering the aircraft to the goal. The strategy does not rely on a prior map of the environment, or the ability to build a map in real-time, and can be run in real-time on-board the aircraft. We utilize a library of optimal trajectories, both conventional and aerobatic maneuvers, that are solved off-line. A sequence of these trajectories is pieced together to form a collision-free motion plan within the field of view of the depth camera that steers the aircraft towards the goal region. We validate the approach in a high-fidelity simulation environment. The aircraft flies autonomously through a forest-like map to a goal region, using conventional maneuvers such as banked and helical turns, as well as aerobatic maneuvers such as an aggressive turnaround.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".