Three-Dimensional Near-Seabed Path Planning for Autonomous Underwater Vehicles Using Fisher Information and Slope–Curvature–Yaw-Rate Constraints

Autonomous underwater vehicles (AUVs) operating near the seabed must maintain localization reliability while satisfying maneuverability and bottom-clearance constraints. This study proposes a three-dimensional near-seabed path-planning framework integrating likelihood-function-curvature Fisher information, multi-criteria start-point selection, an information-guided Hybrid A* planner, vertical-plane homotopy correction, and closed-loop tracking validation. Terrain adaptability is quantified from the curvature of the local likelihood surface and interpreted through the associated Cramér–Rao lower bound (CRLB). For the test area, a case-specific adaptability threshold of 0.6 was selected by balancing the mean CRLB against the usable planning area. Across three start-point schemes, the proposed planner increased mean terrain adaptability to 0.26–0.31 and reduced traversal through low-adaptability regions by 2.8–16.2 percentage points, with only a 0–1.9% increase in path length relative to standard Hybrid A*. After vertical correction, the generated trajectory satisfied the prescribed yaw-rate, slope, and curvature limits. Dynamic simulations of the HHU-1 AUV yielded horizontal and three-dimensional tracking RMSEs of 1.50 m and 2.51 m, respectively. Paired TAN-EKF Monte Carlo tests further showed a 3.32% nominal reduction in horizontal localization RMSE and reductions of 3.37–4.09% under four disturbance scenarios, indicating a modest but statistically consistent localization benefit.