Precision Modeling and Optimally-safe Design of Quadcopters for Controlled Crash Landing in Case of Rotor Failure
Bibliographic record
Abstract
The seminal work cited in [1],[2] showed, for the first time, that flight stability of quadcopters would be possible in case of one or even multiple rotor failures. However, the quadcopter can remain airborne only by going through a spinning maneuver about an axis, fixed w.r.t the vehicle (i.e., resolved yaw). Furthermore, positional control can be achieved by periodically tilting this axis. This paper builds upon this concept with two major improvements: (1) introducing a precise aerodynamic model of propellers that takes the flapping torque due to unbalanced lifting force in the advancing and retreating blades subjected to freestream, into account, and (2) adding to the stability and flight efficiency of the quadcopter by introducing symmetric fixed tilting angles to the trust vectors. In our previous work [3], it was shown how the flight stability and energy efficiency can be improved by introducing fixed tilting angles in the thrust vectors. For controlled crash landing in case of one rotor failure, where a resolved yaw maneuver would be inevitable, introducing a titling angle in rotors can generate a reasonable resolved-rate-yaw spinning speed to keep the quadcopter airborne at a lower rotational speed of the blades by taking advantage of the freestream generated by spinning. This tilting angle would also lead to passive stability in yaw motion of the quadcopter before the failure. Our hypothesis was successfully tested via simulations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".