Autonomous Underwater Vehicle (AUV) Motion Design: Integrated Path Planning and Trajectory Tracking Based on Model Predictive Control (MPC)
Bibliographic record
Abstract
This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".