AUV-WTN: AUV Water Tunnel Navigation Framework with Acoustic Perturbations and Narrow Space Constraints

Abstract

In water tunnels, autonomous navigation of autonomous underwater vehicles (AUVs) is challenging under accumulated localization errors and severe acoustic perturbations constraints. An AUV water tunnel navigation (AUV-WTN) framework is proposed to address these challenges. AUV-WTN integrates a forward-looking sonar (FLS) image segmentation method based on the refined mask R-CNN (RM R-CNN) network with real-time trajectory planning that employs the dynamic trajectory homotopy method (DTHM). RM R-CNN is optimized to combine a mixed-frequency block (MFB) along with a weighted loss function. Additionally, precise region of interest pooling (PrRoI Pooling) is combined to mitigate the impact of false targets, blurred edges, and noise on segmentation accuracy. DTHM is proposed to reduce trajectory drift by dynamically updating path generation based on segmented FLS images. Experimental results demonstrate that RM R-CNN outperforms state-of-the-art (SOTA) methods, achieving a 10.9% improvement over Mask R-CNN in mask segmentation. The simulation platform and real AUV experiments indicate that the capability of AUV-WTN framework is effective in generating precise paths and ensuring collision-free navigation in tunnel environments.