Enhanced auto pilot for marine applications with adaptive speed control
Bibliographic record
Abstract
Today's society is highly focused on safety and environment, this together with ride comfort are used as selling points for autonomous cars.Boats are also vessels commonly used in traffic and therefore have similar rules and guidelines to consider, as well as being seen as a symbol of luxury and a comfortable life.Autopilots have been used in the marine sector a long time, but only to keep bearing of the boat.This project's aim is to implement a speed controller for boats to be used together with existing autopilots, to reach a higher level of comfort and also reduce the number of stressful tasks for the captain.A simulation environment is build up in Simulink and Matlab to be able to test and develop the controller without having to do sea trials.In the simulation environment a physics engine is built to represent a boat's movement in six degrees of freedom where also two different boats are modeled.Instead of using existing autopilots in the simulation environment a new steering algorithm has been developed.In simulation the speed controller showed great potential, especially in more curvy routes.It managed to increase comfort and path following ability without increasing the run time to finish the route.However, while doing sea trials and using existing autopilots to control the heading of the boat the result was very different.From the results of the sea trials it is concluded that today's autopilots are not advanced enough to run a multi point route with active speed control.Instead, the steering and speed controls would benefit from being developed in conjunction with each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".