Simulation and Experimental Validation of an Autonomous Perching and Takeoff Method for a Multirotor UAV on Vertical Surfaces using a Suction Cup
Bibliographic record
Abstract
This paper details the simulation and experimental validation of an autonomous perching and take-off method for a multirotor unmanned aerial vehicle (UAV) using a suction cup perching mechanism on vertical surfaces. The suction cup interaction with different surface types is characterized with experimental tests to accurately model the perching manoeuvre. The resulting model is used to develop a realistic hardware-in-the-loop (HIL) simulation of the perching and take-off manoeuvre of the UAV in Gazebo. A control method is developed to automate the perching and take-off manoeuvre. The method is tested in simulation and is experimentally validated with flight tests. Comparisons between simulation and experimental data demonstrate that the simulation is accurate and can be used to continue the development of autonomous perching methods.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".