Bibliographic record
Abstract
Abstract By virtue of distinguished wing shape morphing characteristics, the unrivaled agility and flight maneuverability of bats have inspired scientists and engineers to develop novel forms of robots that can fly like bats. The unique wing conformations, flight kinematics, and aerodynamics offer significant advantages over the conventional form of miniature air vehicle in terms of quiet, safe operations, improved efficiency, and enhanced maneuverability. Meanwhile, they also pose substantial challenges for robot design from multiple perspectives, including mechanical design, sensing, control, etc. The practical benefits and technical bottleneck have motivated the development of bat-inspired robots in recent years. The purpose of this paper is to summarize the designing principles and report current state-of-the-art of bat-inspired robot designs, emphasizing the respective distinguishing features of each paradigm, along with the room for further improvement. Rather than showcasing advancement in wing materials, we will focus on the mechanical design and control methodology. This paper will help researchers new in this realm to get familiar with the bat-inspired robots by adopting features from existing designs. It also concludes technical challenges associated with future development, involving biological research, aerodynamic modeling, mechanical design, and control technique.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".