Obstacle Avoidance for a Large-Scale High-Speed Underactuated AUV in Complex Environments
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it