A wave simulator and active heave compensation framework for demanding offshore crane operations
Bibliographic record
Abstract
In this work, a framework is presented that makes it possible to reproduce the challenging operational scenario of controlling offshore cranes via a laboratory setup. This framework can be used for testing different control methods and for training purposes. The system consists of an industrial robot, the Kuka KR 6 R900 SIXX (KR AGILUS) manipulator and a motion platform with three degrees of freedom. This work focuses on the system integration. The motion platform is used to simulate the wave effects, while the robotic arm is controlled by the user with a joystick. The wave contribution is monitored by means of an accelerometer mounted on the platform and it is used as a negative input to the manipulator's control algorithm so that active heave compensation methods can be achieved. Concerning the system architecture, the presented framework is built on open-source software and hardware. The control software is realised by applying strict multi-threading criteria to meet demanding real-time requirements. Related simulations and experimental results are carried out to validate the efficiency of the proposed framework. In particular, it can be certified that this approach allows for an effective risk reduction from both an individual as well as an overall evaluation of the potential harm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".