Robust and efficient tracking and mapping are critical for underwater vehicles, but remain challenging due to degraded visual quality, ambiguous features, and limited computational resources. Although recent deep learning-based stereo matching methods have significantly improved geometric perception for robots, most existing approaches struggle to simultaneously achieve high speed and strong generalization. To address these challenges, we propose SAFT, a tracking and mapping framework based on self-supervised, robust, and real-time stereo matching. SAFT introduces three key innovations: 1) SAFT-Stereo, a novel stereo matching network that integrates cost aggregation with iterative optimization to enable efficient disparity estimation in feature-sparse regions; 2) a spatiotemporal self-supervised loss that leverages both spatial and temporal constraints to provide stable training signals in textureless regions; and 3) SAFT-DSOL, a real-time tracking and mapping algorithm that integrates the self-supervised models to achieve robust localization and dense reconstruction. Extensive experiments on both public and custom underwater datasets demonstrate that SAFT-Stereo achieves the best generalization performance among all real-time methods, while requiring only 1/6 of the inference time of RT-IGEV++. Moreover, the proposed SAFT-DSOL enables stable and efficient tracking and achieves real-time dense reconstruction in indoor shipwreck scenarios. The code is available at github.com/c237814486/SAFT-Stereo.

Tactile sensing is indispensable for underwater operations, as it provides critical feedback on contact information and environmental interactions. Although existing sensors, such as optical, piezoelectric devices, have been employed for underwater tactile perception, they exhibit some limitations in multi-physical field contact perception. To address these challenges, this study introduces the ViTac-Gripper, a vision-based tactile sensing underwater gripper designed to enhance underwater perception and grasping capabilities. The Finite Element Method (FEM) is utilized to simulate the deformation and force distribution on the tactile surface, providing a high-fidelity model for contact behavior analysis. A domain-aligned multi-physical information perception network framework is proposed, which effectively bridges the simulation-to-reality gap and enables robust extraction of tactile information, including normal/shear force, contact position, 3D Reconstruction and dense force distribution. Experimental results demonstrate the system’s ability to accurately reconstruct multi-physical contact information, while the adaptive grasping strategy ensures stable and reliable object manipulation. The ViTac-Gripper represents a significant advancement in underwater robotics, offering a cost-effective and versatile solution for underwater manipulation tasks.

Underwater vehicle-manipulator systems (UVMSs) play crucial roles in the fields of underwater target monitoring and pipeline maintenance. However, achieving accurate tracking for underwater pipelines is challenging due to the complexity of UVMSs in terms of nonlinearity, strong coupling and underactuation. To solve the aforementioned problems, an underwater biomimetic vehicle-manipulator system (UBVMS) and an underwater pipeline tracking control method based on the robot vision are proposed. The UBVMS is equipped with the biomimetic undulatory fin propulsors and the biomimetic flipper propulsors, which are inspired by the median and/or paired fin propulsion mode and the body and/or caudal fin propulsion mode of fishes, respectively. The biomimetic undulatory fin propulsors provide the UBVMS with advantages of maneuverability and stability, while the biomimetic flipper propulsors enable the UBVMS to have improved acceleration ability. A tracking control algorithm with adaptive weight coefficients is designed to improve the pose stability of the UBVMS. A fuzzy rule mapping model is constructed to describe the nonlinear relationship between the biomimetic propulsors' control parameters and the propulsive force/torque. Finally, four types of pipeline tracking experiments are conducted to verify the effectiveness and feasibility of the proposed UBVMS and control algorithm.

The complex underwater environment presents numerous challenges for the design of soft grippers, which often suffer from limited load capacity, poor stability, low portability, and imprecise control. This paper proposes a novel rigid-soft hybrid gripper specifically designed for underwater use. The gripper's finger is constructed from silicone, reinforced with a multi-link rigid exoskeleton on the outside, and actuated by tendons. This design provides three key advantages: compliance (capable of handling fragile objects such as a piece of tofu), heavy lifting (demonstrated by lifting an 80 kg barbell with three fingers), and precise, stable operation (the hybrid gripper maintains its shape despite water flow disturbances). Additionally, the gripper is compact and lightweight, with the driving system powered by just four 23g servo motors, making it easy to mount on various underwater robots. To enable precise control, both specialized kinematic and mechanics models were developed, allowing accurate predictions of the relationships among tendon displacement, exoskeleton deformation, soft material deformation, and tendon tension. This study thoroughly considers the challenges of underwater environments, offering new insights for advancing the field of underwater soft grasping.