uWSC Aircraft Simulator: A Gazebo-based model for uncrewed weight-shift control aircraft flight simulation
Bibliographic record
Abstract
Weight-shift control (WSC) microlights are a class of aircraft that maneuver by manipulating a mass attached to deforming flexible wings, which are favoured by many recreational pilots due to their simple mechanical structure, superior low-speed maneuverability, positive aerodynamic stability, and capability to operate on rugged terrains with minimal infrastructure requirements for take-off and landing. However, the dependency on human-powered control restricts their effective payload and mission scope. Here, we present an uncrewed weight-shift control (uWSC) aircraft simulator model developed within the open-source robotics simulator Gazebo. The aim of the model is to spur further study of weight-shift aircraft as a new class of uncrewed aerial systems. The model’s physical properties are based upon an uncrewed electrically powered hang-glider prototype. The longitudinal and lateral positive static aerodynamic stability of the flexible hang-glider wing is approximated by a rigid wing consisting of multiple joined sections. The performance of the uWSC aircraft model is benchmarked against data captured from real weight-shift aircraft flight tests. Results indicate that the simulator model provides a suitable approximation of real weight-shift aircraft flight. The simulator’s capabilities in emulating weight-shift control mechanisms in flight is indicative of its utility in bridging higher-level flight controller design with lower-level weight-shift control dynamics, opening new directions towards the autonomous and semi-autonomous operations of uWSC by allowing quick testing for design and prototype innovation and improvement.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.006 | 0.001 |
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".