Dynamics of a quadrotor undergoing impact with a wall
Bibliographic record
Abstract
In this paper, we investigate the problem of the dynamics of a quadrotor unmanned aerial vehicle undergoing impact with its environment. This work is motivated by the fact that operation of UAVs (manual or autonomous) carries with it a significant risk of collision with surrounding objects, particularly in unknown, unstructured environments. To make small UAVs more viable and expand their autonomy, our ultimate objective is to develop control methods which would allow automatic recovery from a `non-destructive' collision, where operation of the vehicle is not compromised. Towards this goal, we formulate the dynamics model of a quadrotor equipped with protective bumpers around its propellers, undergoing an arbitrary collision with a vertical wall: no prior assumptions are made regarding the points and number of impacts, nor the impact speed, nor the orientation of the vehicle at the instance of collision. The model is exercised through a series of simulations for different pre-impact attitudes of the platform and different approach speeds. Results of experimental tests conducted with Spiri quadrotor platform are presented. Comparison to the simulated responses mimicking the experimental pre-impact conditions and command inputs show excellent qualitative agreement.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".