Friction Shock Absorbers and Reverse Thrust for Fast Multirotor Landing on High‐Speed Vehicles
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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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) | 0.001 | 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".