SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping

2026-05-14

AI Summary

This study addresses the stringent demands on localization accuracy and robustness imposed by high-resolution seafloor mapping, a task for which conventional control methods often underperform under complex sea conditions. Focusing on the SeaVis remotely operated towed vehicle (ROTV), the authors develop a high-fidelity dynamic model and propose a gain-scheduled linear quadratic regulator (LQR) control strategy. This approach enables robust depth and attitude control across the full operating speed range, significantly enhancing disturbance rejection while reducing actuator effort. Simulation results demonstrate that, compared to a traditional PID controller, the proposed method achieves superior control efficiency, greater robustness, and lower energy consumption when navigating complex seabed terrains, thereby confirming its strong potential for practical engineering applications.

Abstract

High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for SeaVis remotely operated towed vehicles (ROTVs) and develops a gain-scheduled linear-quadratic regulator (LQR) for robust depth and attitude control. We validate the approach in a high-fidelity simulation, benchmarking the LQR against a conventional PID controller over a challenging seabed profile. The presented results demonstrate the LQR's superior performance, with significantly enhanced robustness to disturbances, greater control efficiency, and substantially reduced flap actuation. The gain scheduling also confirms the controller's effectiveness across the full operational velocity range. The complete simulation environment and controller are open-sourced.

Problem

• seafloor mapping
• remotely operated towed vehicle
• depth and attitude control
• underwater robotics
• robust control

Innovation

• gain-scheduled LQR
• remotely operated towed vehicle
• seafloor mapping
• robust control
• underwater robotics