Instrumentation and self-repairing control for resilient multi-rotor aircrafts
Bibliographic record
Abstract
Purpose Even though multi-rotor aircrafts are becoming more and more prevalent in the fields of aerial photography, agricultural spraying, disaster searching and rescuing, how to achieve higher reliability and robustness of an aircraft still poses a big challenge. It is not a rare case that a multi-rotor aircraft is severely damaged or crushed when an actuator or sensor is malfunctioned. This paper aims at the resilience of an aircraft when a rotor is malfunctioned. Design/methodology/approach The reliability of a multi-rotor aircraft can be measured in terms of stability, robustness, resilience and fault tolerance. All of these four aspects are taken into consideration to improve overall reliability of aircrafts. When a rotor malfunction occurs, the control algorithm is cable of adjusting the operation conditions of the rest of rotors to achieve system stability. Findings In this paper, the authors first present a research topic on the development of a resilient multi-robot aircraft. A multi-rotor aircraft usually possesses more actuated motions than the required degrees of freedom. Originality/value The authors proposed to equip the multi-rotor aircraft with malfunction detecting sensors, and they developed the self-repairing algorithm to re-stabilize the aircraft when a malfunction of a rotor occurs. The design concept and methods were implemented on an eight-rotor aircraft, and the performance of the proposed instrumentation and self-repairing algorithm have been verified and validated.
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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 | 0.001 | 0.000 |
| 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".