Model Helicopter Control Using Body-Mounted Vibro-Tactile Transducers
Bibliographic record
Abstract
The sense of touch as a man-machine communication channel can be as acute as the sense of sight and sound. In some scenarios such as those seen in aerobatics, stunt flying, and combat flights, tactile sensors can even outperform the conventional non-contact sensors in terms of situation awareness. Fusion of tactile sensory information with those obtained via sight and sound can avoid diverting the user’s attention away from the operational task at hand as well. In this study, the performance of an operator, to servo control the motion of a 2-dof model helicopter with pitch/yaw maneuverability, subjected to an intuitive body-referenced arrangement of a cluster of vibro-tactile sensors is investigated. A blindfolded operator will then control the helicopter to a safe attraction zone via a joystick based on this tactile sensory information. A fine-tuned local controller would take over for the end-of-motion precise homing. This study can pave the way towards a systematic integration and characterization of tactile sensors in high performance weapon platforms with improved situation awareness in visually awkward maneuvers such as those seen in aerial combat scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".