Application of On-Board Evolutionary Algorithms to Underwater Robots to Optimally Replan Missions with Energy Constraints
Bibliographic record
Abstract
The objective is to show that on-board mission replanning for an AUV sensor coverage mission, based on available energy, enhances mission success. Autonomous underwater vehicles (AUVs) are tasked to increasingly long deployments, consequently energy management issues are timely and relevant. Energy shortages can occur if the AUV unexpectedly travels against stronger currents, is not trimmed for the local water salinity has to get back on course, and so forth. An on-board knowledge-based agent, based on a genetic algorithm, was designed and validated to replan a near-optimal AUV survey mission. It considers the measured AUV energy consumption, attitudes, speed over ground, and known response to proposed missions through on-line dynamics and control predictions. For the case studied, the replanned mission improves the survey area coverage by a factor of 2 for an energy budget, that is, a factor of 2 less than planned. The contribution is a novel on-board cognitive capability in the form of an agent that monitors the energy and intelligently replans missions based on energy considerations with evolutionary methods.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".