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.

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.

Aiming at the current challenges in converting electrical parameters to indicator diagrams, a subspace-aided indicator diagram estimation approach is proposed to establish a data-driven mapping model from electrical to force parameters, which helps avoid the need for analyzing the mechanism model of tower-type pumping systems. Specifically, the lifting technique is adopted based on the subspace method to construct the space of the electrical parameter signals, addressing the correspondence between input and output signals, while preventing the loss of effective information. Then, a recursive indicator diagram estimation approach is proposed, utilizing the updating/downdating of the Cholesky decomposition to enable online updating of the data-driven mapping model. In addition, for tower-type pumping systems operating under multiple conditions, a gap metric indicator is developed as a test statistic to determine the switching of operating conditions. The effectiveness of the proposed methods is verified through experimental measurements from tower-type pumping systems in actual oil wells.

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.

Different work modes of phased array radar have different threat levels. Therefore, it is of great significance to distinguish the work mode of the phased array radar by the intercepted information. However, as the phased array radar signals are non-cooperative signals, it is difficult to identify them in terms of in-pulse information. Traditional methods do not have enough analysis on the radar signals and heavily rely on machine learning algorithms to identify the work mode, so the accuracy is low. By utilizing the smoothness differences, backlight information and pulse repeat frequency variation rule, in this paper, we use the deep learning method to identify seven common work modes of airborne phased array radar. The simulation results show that the method has a high identification accuracy for the work mode of phased array radar.

We develop two cooperative robots for power transmission lines (PTLs) inspection - a light climbing robot (CBR) which can stably move on the overhead ground wire (OGW) for sensor data collection and an unmanned aerial vehicle (UAV) with a grabbing mechanism, which can automatically put the CBR on the OGW and take it off. In order to guarantee the safety, the mechanical structures of the connectors are designed in the shape of a trumpet. Further, a self-locked structure of the CBR is developed to automatically seize and release the OGW. For autonomous navigation, the UAV is equipped with a movable sliding rail and a 2D Laser Range Finder (LRF). The LRF can not only detect the position and orientation of the OGW but also detect the top beam of the CBR and the grabbing position in it. Furthermore, the action of the grabbing mechanism is automatically triggered by a microswitch. Finally, by the developed UAV and CBR platforms, we test the whole loading and unloading strategy in an artificially constructed PTLs environment outdoors and achieve an encouraging result1. Combining the flexible motion of the UAV and the high inspection accuracy of the CBR, the CBR can negotiate any obstacle by flying and abandon the traditional heavy obstacle crossing mechanism to effectively realize close-proximity inspection. Due to the light weight and low power consumption, the CBRs can be deployed once in many power corridors to conduct a long-term inspection.

This paper proposes a unique and robust UAV autonomous navigation approach along one side of overhead transmission lines for inspection. To this end, we establish a perspective model and develop a novel Pan/Tilt monocular-based navigation scheme. Simultaneously, the following three key issues are addressed. First, to locate the effective landmark - transmission tower timely and reliably, we customize a neural network for tower detection and combine it with a fast and smooth tracking. Second, to provide UAV with a robust and precise heading, we detect the transmission lines and compute and optimize their vanishing point. Third, to keep a safe distance from transmission lines, we optimize a homography matrix to restore the parallel nature of transmission lines and perceive the distance variation by a point set registration model. Finally, by the designed UAV platform, we test the whole system in a real-world transmission-line inspection scenario under different weather condition and achieve an encouraging result. Our approach provides great flexibility for refined inspection and effectively improves inspection safety.