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 ℒ2gain 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".