Friction Shock Absorbers and Reverse Thrust for Fast Multirotor Landing on High‐Speed Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Typical landing gears of small uninhabited aerial vehicles (UAV) limit their capability to land on vehicles moving at more than 20–50 km/h due to high drag forces, high pitch angles and potentially high relative horizontal velocities. To enable landing at higher speeds, a combination of lightweight friction shock absorbers and reverse thrust was developed. This allows for rapid descents (i.e., 3 m/s) toward the vehicle while leveling at the last instant. Simulations show that the proposed system is (1) more robust at higher descent speeds contrary to traditional configurations, (2) can touchdown at almost any time during the leveling maneuver, thus reducing the timing constraints, and (3) is robust to many environmental, design and operational factors, maintaining a success rate above 80% up to 100 km/h. Compared to standard multirotors, this approach expands the possible state envelope at touchdown by a factor of 60. A total of 38 experimental trials were conducted where a drone successfully landed on a pickup truck moving at speeds ranging from 10 to 110 km/h. The increased touchdown envelope was shown to improve the multirotors' robustness to external disturbances such as winds and wind gusts, sensor errors and unpredictable motion of the ground vehicle. The increased landing capabilities also expand the flight envelope at the start of the leveling maneuver by a factor of 38 compared to a standard multirotor, thereby allowing the drone to fly in tougher conditions and initiate its leveling maneuver from a broader range of altitudes, vertical and horizontal velocities, as well as pitch angles and rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it