REAL-TIME OBSTACLE AVOIDANCE FOR AN UNDERACTUATED FLAT-FISH TYPE AUTONOMOUS UNDERWATER VEHICLE IN 3D SPACE
Bibliographic record
Abstract
This paper discusses a real-time obstacle avoidance algorithm and its implementation for an underactuated flat-fish type autonomous underwater vehicle (AUV) in 3D space. The algorithm has been developed using multi-point potential field (MPPF) method and its real-time testing is carried out using hardware-in-loop (HIL) simulations. In MPPF method, a region of predefined radius on a hemisphere in the positive x-axis around the bow of an AUV is discretized into equiangular points with centre as the current position. By determining the point at which the minimum total potential exists, the vehicle can be moved towards that point. Here the analytical gradient of the total potential function is not calculated as it is not essentially required for moving the vehicle to the next position. The MPPF method is interfaced with dynamic model of an underactuated flat-fish type AUV and it is tested and verified using HIL simulation tool. The details of the dynamics of AUV, MPPF method and its implementation, development of HIL test bench and the simulation results are presented in this paper. The results show that the proposed MPPF method is very effective for obstacle avoidance in 3D space and can be used in the real-time control of the AUV.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".