Research on Real Time Obstacle Avoidance Method for AUV Based on Combination of ViT-DPFN and MPC

Due to the complex underwater environment, autonomous underwater vehicles (AUVs) may fail due to unknown obstacles in the process of performing tasks. Therefore, studying the real-time obstacle detection and avoidance methods for AUVs based on forward-looking sonar is very important. This article proposes a set of obstacle avoidance algorithms based on the images collected by the multibeam forward-looking sonar carried by AUVs. First, a new obstacle detection network based on a dual-path feature network based on a vision transformer (ViT-DPFN), which combines a convolutional neural network (CNN) and transformer, is proposed to detect obstacles in sonar images quickly and accurately. Then, combined with the current attitude data of AUVs, the obstacle position collected by the sonar image is corrected. Finally, using the fixed obstacle size and position data, the cost function is established based on the model predictive control (MPC) method, the constraint conditions are designed, and the external collision avoidance constraint is established so that AUVs can avoid obstacles in the trajectory tracking process. The simulation and field experiment results show that the proposed method improves the obstacle detection accuracy and processing speed and ensures the navigation safety of AUVs in complex obstacle environments, proving the proposed method's advancement and effectiveness. The underwater acoustic target detection (UATD) dataset is available at https://github.com/zhenghaotianHEU/UATD-sonar-dataset.