Reactive Obstacle-Avoidance for Agile, Fixed-Wing, Unmanned Aerial Vehicles
Bibliographic record
Abstract
Agile, fixed-wing, aircraft have been proposed for diverse applications, due to their enhanced flight efficiency, compared to rotorcraft, and their superior maneuverability, relative to conventional, fixed-wing, aircraft. We present a novel, reactive, obstacle-avoidance algorithm that enables autonomous flight through unknown, cluttered environments using only on-board sensing and computation. The method selects a reference trajectory in real-time from a pre-computed library, based on goal location, instantaneous point cloud data, and the aircraft states. At each time-step, a cost is assigned to candidate trajectories that are collision-free and lead to the edge of the obstacle sensor’s field-of-view, with cost based on both distance to obstacles, and the goal. The lowest cost reference trajectory is then tracked. If all potential trajectories result in a collision, the aircraft has enough space to come to a stop, which theoretically guarantees collision-free flight. Our work demonstrates autonomous flight in unknown and unstructured environments using only on-board sensing (stereo camera, IMU, and GPS) and computation with an agile, fixed-wing, aircraft in both simulation and outdoor flight tests. During flight testing, the aircraft cumulatively flew 4.4km autonomously in outdoor environments with trees as obstacles with an average speed of 8.1ms−1 and a top speed of 14.4ms−1. To the best of our knowledge, ours is the first obstacle-avoidance algorithm suitable for agile, fixed-wing, aircraft that can theoretically guarantee collision-free flight and has been validated experimentally using only on-board sensing and computation in an unknown environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 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".