Stable Haptic Shared Autonomy for Wall Landing of Two-Wheeled Drones via Control Barrier Functions
Bibliographic record
Abstract
This paper presents a novel control method using Haptic Shared Autonomy (HSA) for two-wheeled drones that can drive on surfaces and fly in the air. These drones can switch between flight and contact-based locomotion, enabling versatile operation in complex environments, but tend to become unstable when transitioning from flight to wall-running mode due to wall-collision impacts. To address this, we formulate a human-in-the-loop framework in which the operator inputs commands and receives haptic feedback via a haptic device. We design Control Barrier Functions (CBFs) and impose an energy-based ℒ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> gain constraint to guarantee soft landing. By solving a Sequential Control Force (SCF) problem, we compute the drone’s control inputs and haptic feedback forces, automatically adjusting the approach speed. Simulations demonstrate that the proposed method achieves effective soft wall landings while preserving the human operator’s intent.
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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) | 0.001 | 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) | 0.004 | 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".