Inclined landing testing of an omni-directional unmanned aerial vehicle
Bibliographic record
Abstract
Traditional multi-rotors and helicopters control translational movement by changing the orientation of the entire vehicle. This approach limits the effectiveness of these vehicles in applications — such as a mobile manipulator base — in which it is often necessary to fly near large structures where unpredictable aerodynamic conditions exist. Maintaining precise control of position requires counteracting disturbance forces quickly. Having to roll and pitch the entire vehicle induces a delay that limits the ability of traditional multi-rotors and helicopters to maintain position precisely. Additionally, a mobile base must be capable of resisting arbitrary combinations of force and torque resulting from use of a manipulator arm. Traditional multi-rotors and helicopters do not have this ability. This paper presents a novel unmanned aerial vehicle (UAV) concept that features the addition of four rotors orthogonal to the main lift rotors of a traditional quadrotor design. These rotors allow decoupling of orientation from translational movement. Tests done on a physical prototype demonstrated the ability to sustain roll or pitch angles up to 15° independent of translational movement, including the ability to move backwards at an angle and to dock on inclined surfaces in a controlled manner. The tests show that the OmniCopter can successfully land on surfaces with up to 30° inclination. This type of motion is impossible for a traditional multi-rotor vehicle.
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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".