Simulation for path planning of autonomous underwater vehicle using Flower Pollination Algorithm, Genetic Algorithm and Q-Learning
Bibliographic record
Abstract
The motivation behind this paper is to address the necessity for exploration in near bottom ocean environment employing Autonomous Underwater Vehicles. This paper presents a simulation for an optimized path planning for an autonomous underwater vehicle in benthic ocean zones. The statistical data pertaining to the near-bottom ocean currents has been sourced from the Bedford Institute of Oceanography, Canada. A cost function is developed which incorporates the interaction of the underwater vehicle with the ocean currents. This cost function takes the source and destination coordinates as the inputs and outputs the time taken by the vehicle to travel between them. This paper aims to minimize this cost function to obtain a path having the least travel time for the vehicle. Various biologically inspired algorithms such as Flower Pollination Algorithm and Genetic Algorithm have been used to optimize this cost function. The optimization of the cost function has also been performed using Q-Learning technique and the results have been compared with the biologically inspired algorithms. The results depict that Q-Learning Algorithm is better in computational complexity and ease of simulating the environment. Thus, an efficient Path planning technique, which has been tested for the cost function of an autonomous underwater vehicle is proposed through this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".