Uncrewed Surface Vessel Testbed and Gripping Mechanism for Drone Landings in Harsh Conditions
Bibliographic record
Abstract
Autonomous unmanned aerial vehicles (UAVs) and uncrewed surface vessels (USVs) have the potential to greatly improve ocean monitoring, search and rescue, and scientific data collection. Autonomously coordinating their operations could extend mission range and allow exploration of remote areas; however, a major challenge in landing a UAV on a moving USV in harsh conditions has not been solved. Previous work in the Robora Lab developed a mobile two-axis tilting platform with pitch and roll to emulate USV motion in waves to test novel landing algorithms outside of simulation. This project expanded on that work by integrating a USV with custom control for future on-water algorithm testing. In addition, a proof-of-concept mechanical solution was developed to enable robust UAV–USV landings. The BlueRobotics BlueBoat USV was acquired and its basic features were validated in the water. It was upgraded with a Jetson Orin Nano to communicate with the existing autopilot and onboard sensors to coordinate real time control. A custom Model Predictive Control algorithm was developed and implemented in cascade with existing onboard controllers to demonstrate custom control while minimizing tuning efforts. These upgrades enable the BlueBoat to be a capable testbed for future autonomous UAV-USV interaction experiments. Additionally, a novel corkscrew-based gripping mechanism was developed to enable reliable, toggleable adhesion between a UAV and landing platform. The mechanism uses a motorized corkscrew to engage with hook-and-loop material, securing the UAV during landing while allowing controlled release for takeoff. The system was integrated with a CrazyFlie UAV and evaluated with the two-axis tilting platform. Varying descent speeds, corkscrew actuation parameters, and platform motions were tested. The mechanism was found to increase the success rate of landings and takeoffs compared to a control, especially on highly tilted platforms upwards of 45 degrees, or during rapid descents.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".