An Aerial–Aquatic Hitchhiking Robot with Remora‐Inspired Tactile Sensors and Thrust Vectoring Units
Bibliographic record
Abstract
Hybrid aerial–aquatic robots can operate in both air and water and cross between these two. They can be applied to amphibious observation, maritime search and rescue, and cross‐domain environmental monitoring. Herein, an aerial–aquatic hitchhiking robot is proposed that can fly, swim, and rapidly cross the air–water boundaries (0.16 s) and autonomously attach to surfaces in both air and water. Inspired by the mechanoreceptors of the remora (Echeneis naucrates) disc, the robot's hitchhiking device is equipped with two flexible bioinspired tactile sensors (FBTS) based on a triboelectric nanogenerator for tactile sensing of attachment status. Based on tactile sensing, the robot can perform reattachment after leakage or adhesion failure, enabling it to achieve long‐term adhesion on complex surfaces. The rotor‐based aerial–aquatic robot, which has two thrust vectoring units for underwater locomotion, can maneuver to pitch, yaw, and roll 360° and control precision motion position. The field tests show that the robot can continuously cross the air–water boundary, attach to the rough stone surface, and record video in both air and underwater. This study may shed light on future autonomous robots capable of intelligent navigation, adhesion, and operation in complex aerial–aquatic environments.
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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".