Quadrotor collision characterization and recovery control
Bibliographic record
Abstract
Collisions between quadrotor UAVs and the environment often occur, for instance, under faulty piloting, from wind gusts, or when obstacle avoidance fails. Airspace regulations are forcing drone companies to build safer drones; many quadrotor drones now incorporate propeller protection. However, propeller protected quadrotors still do not detect or react to collisions with objects such as walls, poles and cables. In this paper, we present a collision recovery pipeline which controls propeller protected quadrotors to recover from collisions. This pipeline combines concepts from impact dynamics, fuzzy logic, and aggressive quadrotor attitude control. The strategy is validated via a comprehensive Monte Carlo simulation of collisions against a wall, showing the feasibility of recovery from challenging collision scenarios. The pipeline is implemented on a custom experimental quadrotor platform, demonstrating feasibility of real-time performance and successful recovery from a range of pre-collision conditions. The ultimate goal of the research is to implement a general collision recovery solution as a safety feature for quadrotor flight controllers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".