Objectives Existing works mainly cover low-speed navigation for small- to medium-sized Autonomous Underwater Vehicles (AUVs) and often oversimplify internal and external constraints. There is a lack of research addressing high-speed 3D obstacle avoidance for large-scale AUVs under limited Field of View (FoV) and complex constraints. To address this gap, an online 3D obstacle avoidance scheme for large-scale high-speed AUVs is proposed.
Methods This 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. Subsequently, a dynamic perception framework incorporating filters, feature extraction and matching is designed to achieve motion prediction of unknown moving obstacles. Next, global risk-aware path searching and local spatial-temporal trajectory optimization are proposed to generate an aggressive trajectory satisfying constraints. Finally, a spherical-coordinate feedback controller is employed for trajectory tracking.
Results In high-fidelity experiments involving long-range seabed traversal, a 13.96-meter-long AUV flexibly avoids dynamic and static obstacles while adhering to constraints, maintaining a predefined speed of 6.0 m/s.
Conclusions The proposed approach enables the large-scale high-speed AUV to navigate agilely and avoid obstacles safely under limited FoV and multiple constraints, enhancing its operation capabilities.
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