Considering that existing research on autonomous underwater vehicle (AUV) obstacle avoidance mainly focuses on low-speed obstacle avoidance for small and medium-sized AUVs and overly simplifies the diverse constraints within and outside the system, a real-time three-dimensional high-speed obstacle avoidance method for large AUVs is proposed.

The method integrates perception, planning, and control modules, enabling large-scale, high-speed, underactuated AUVs to navigate safely and efficiently through the unknown and unstructured ocean floor. First, a robocentric dual-resolution seafloor map is constructed to balance perception accuracy with computational efficiency. Next, a dynamic perception framework incorporating filters and feature extraction and matching is designed to achieve the motion prediction of unknown moving obstacles. Subsequently, global risk-aware path searching and local spatial-temporal trajectory optimization are introduced to generate an aggressive trajectory that satisfies multiple constraints. Finally, a spherical-coordinate feedback controller is employed for trajectory tracking.

In high-fidelity experiments involving long-range seabed traversal, a 13.96-meter-long AUV flexibly avoids dynamic and static obstacles while adhering to the constraints, maintaining a predefined speed of 6.0 m/s.

The proposed approach enables the large-scale high-speed AUV to navigate agilely and avoid obstacles safely under limited field of view and multiple constraints, enhancing its operation capabilities.