Underwater autonomous grasping robot based on multi-stage Cascade DetNet

Yong Zhang, Chengyang Zhang, Bo Li, Yongli Hu, Baocai Yin

Abstract: At present, underwater exploration and salvage, underwater archaeology, and other underwater operations still mainly rely on professional underwater operators. Considering that artificial underwater operation is faced with the problems of small exploration scope, poor working environment, and low work efficiency, it is the future trend to use robots to replace manual underwater operation in related fields. Most of the current underwater robots are artificial remote-controlled, which lack intelligent detection and autonomous grasping system. In this paper, a grasping robot equipped with an AI computing platform is developed to enable the autonomous grasping of underwater targets by using stereo vision technology. For the problem of difficult detection due to the small size and occlusion of underwater targets, this paper proposes Cascade DetNet, which can improve recognition accuracy. The experimental results show that our proposed method achieves the best performance on URPC dataset compared with several mainstream methods. In addition, we also carry out the autonomous grasping of seafood in a real marine environment to verify the autonomous grasping performance of underwater vehicles.