Underwater localization is necessary for autonomous operation of underwater robots. Limited field of view for onboard vision systems can result in poor reliability of visual localization for underwater robots during autonomous docking. This article proposes a dual-type marker fusion-based visual localization method for underwater autonomous docking. First, a visual localization scheme integrating light sources and ArUco markers is designed according to positioning requirements based on varying fields of view for onboard vision in autonomous docking operations. The arrangements of the light sources and ArUco markers are both analyzed and optimized to ensure the reliability and efficiency of visual localization. Then, an underwater visual localization algorithm with dual marker fusion is proposed for high-accuracy localization with a restricted field of view. Extended underwater experiments are conducted to verify the feasibility and stability of the proposed method across various distances. The obtained results provide valuable references for the design and optimization of visual navigation systems for underwater robots.

Three-dimensional information acquisition is crucial for the intelligent control and safe operation of bulk material transportation systems. However, existing visual measurement methods face challenges, including difficult stereo matching due to indistinct surface features, error accumulation in multi-camera calibration, and unreliable depth information fusion. This paper proposes a three-dimensional reconstruction method based on multi-camera hierarchical calibration. The method establishes a measurement framework centered on a core camera, enhances material surface features through speckle structured light projection, and implements a ‘monocular-binocular-multi-camera association’ calibration strategy with global optimization to reduce error accumulation. Additionally, a depth information fusion algorithm based on multi-epipolar geometric constraints improves reconstruction completeness through multi-view information integration. Experimental results demonstrate excellent precision with absolute errors within 1 mm for features as small as 15 mm and relative errors between 0.02% and 2.54%. Compared with existing methods, the proposed approach shows advantages in point cloud completeness, reconstruction accuracy, and environmental adaptability, providing reliable technical support for intelligent monitoring of bulk material transportation systems.

This study explores an efficient hybrid-driven motion planning method for a bionic underwater vehicle equipped with gliding capabilities, aimed at enhancing the efficiency of autonomous docking while ensuring reliable visual navigation. To achieve this, we first construct and analyze an energy-time consumption model along with a visual navigation stability parameter. These frameworks assess the total energy and time expended, as well as the likelihood of target loss during visual navigation. We then develop a hybrid-driven switching scheme that alternates between two motion modes to approach and dock with a stationary target. A novel motion planning method, based on the deep Q network (DQN) algorithm, is proposed to identify the optimal switching points within this scheme. This method aims to minimize energy and time consumption while maintaining visual navigation stability. The efficacy of the proposed method is validated through both simulation and controlled pool experiments. Results indicate that this hybrid-driven approach not only stabilizes visual navigation but also improves the efficiency of autonomous docking when compared to single-mode strategies. This research contributes to the advancement of hybrid-driven autonomous vehicles and offers new perspectives on the motion planning and control of underwater vehicles with gliding capabilities.

When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way.

Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

The optimization method of crystal rotation can effectively improve the temperature distribution on the surface of the melt.