Obstacle Avoidance for a Large-Scale High-Speed Underactuated AUV in Complex Environments
Bibliographic record
Abstract
This paper attempts to develop an integrated guidance and control scheme for obstacle avoidance of a large-scale underactuated autonomous underwater vehicle (LUAUV) with high speed in unknown complex environments. Under a finite field of view of the environmental perceiving sensor, a novel guidance algorithm based on tracking differentiator and receding horizon optimization is proposed to generate a smooth guidance signal, respecting the physical limits on the system state including pitch attitude, velocity, and acceleration. To track the guidance signal and the preset forward velocity accurately, a hierarchical control strategy with kinematics and dynamics levels is raised. At the kinematics level, a robust model predictive control (RMPC) is employed for the vehicle to track the guidance signal and produce a virtual pitch velocity signal. At the dynamics level, an adaptive fast integral terminal sliding mode controller is developed based on the actuated dynamic model of the LUAUV with dynamic uncertainties, matched disturbances, and mismatched disturbances. It can be guaranteed that the tracking errors of the virtual pitch velocity and preset forward velocity locally converge to zero in finite time. Through the high-fidelity visual simulations, the proposed scheme has higher precision, faster single-step solution speed, and stronger robustness than the conventional MPC.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".