This paper presents a novel biomimetic robotic fish prototype incorporating a 2-degree-of-freedom Spherical Parallel Mechanism (SPM) to enable bioinspired caudal fin actuation. The SPM architecture enables orthogonal flapping kinematics that synergistically enhance thrust production and maneuverability through coupled pitch-yaw motions. We systematically present (a) a mechanical system design methodology, (b) a comprehensive 6-DOF hydrodynamic-structural coupling model, and (c) numerical simulations verifying the system's propulsion performance in both forward swimming and turning maneuvers. Simulation results validate the effectiveness of the SPM-based propulsion concept and demonstrate the potential of the proposed biomimetic robotic fish for agile underwater locomotion.

Environmental perception, a cornerstone technology for underwater robotics in marine exploration, confronts significant challenges due to the complexity of underwater environments. This paper systematically reviews recent advancements and trends in underwater environmental perception technologies. Focusing on the two dominant sensing modalities: acoustics and optics, we present key techniques including target detection, positioning, and communication based on acoustic sensing, as well as image enhancement, target recognition and classification, and 3D reconstruction based on optical sensing. The application of multimodal sensor fusion is also explored. Furthermore, an extended discussion evaluates other modalities such as magnetic and electric field sensing, highlighting their potential and constraints in underwater scenarios. Finally, the study synthesizes existing technological bottlenecks and outlines future research directions, serving as a reference for environmental perception in underwater robots.

The American Library Association Core Subject Analysis Committee Subcommittee on Faceted Vocabularies (SSFV) works to advance retrospective implementation of genre/form terms and other faceted metadata in library catalogs. This has included publishing a set of modular best practices for librarians and programmers that comprises specific mappings between Library of Congress Subject Headings (LCSH) and other MAchine-Readable Cataloging data and their corresponding terms in faceted vocabularies. SSFV expects to expand these best practices through successive releases and since 2022 has engaged in testing of its LCSH-to-genre/form term mappings in order to refine them on an ongoing basis. Early rounds of testing helped SSFV identify mappings where human evaluation was needed beyond initial expectations and highlighted the inconsistency of original metadata, underscoring our awareness that mappings are only as useful as the data of the original record. This article discusses the issues of data variance and mapping reliability further and analyzes each in detail. It provides an overview of SSFV's involvement in the development of genre term vocabularies and then discusses the process of mapping development, patterns identified in this process, and the testing procedures, results, and outcomes to show how the evaluation of genre term mappings can inform other types of faceted vocabulary mapping in the future.

The ocean, which is a key component of Earth’s ecosystem, requires advanced technologies for deep and highly comprehensive exploration. Unmanned underwater vehicles (UUVs) play an important role in this task, but their development encounters great challenges due to the complex and dynamic underwater environment. Reinforcement learning (RL) has recently emerged as a promising method to improve the capabilities of UUVs. This study comprehensively reviews the implementations of RL in UUVs, with a focus on key tasks such as motion planning, navigation and control, and multiagent coordination. We investigate current difficulties and emerging trends, as illustrated by a case study. This review aims to provide a foundation for RL-based control and decision-making in UUVs and offer actionable insights for advancing studies in this rapidly evolving domain.

Autonomous navigation in complex 3D underwater environments is crucial for biomimetic robotic fish. However, the traditional Artificial Potential Field (TAPF) suffers from limitations like susceptibility to local minima, target unreachability when obstacles are near the goal, and path oscillations unsuitable for biomimetic locomotion, hindering practical applications. To address these deficiencies, this paper proposes the Dynamic Logarithmic Artificial Potential Field with Adaptive Virtual Guidance (DL-APF-AVG) method, which integrates four key innovations: (1) a logarithmic attraction potential function ensuring target convergence even near obstacles; (2) a dynamic, state-aware repulsion potential considering relative velocity and angle for smoother, proactive avoidance; (3) an Adaptive Virtual Guidance (AVG) strategy to deterministically escape local minima traps; (4) a dynamic step adjustment mechanism to actively suppress path jitter. The synergy of these components enhances target reachability, generates smoother trajectories appropriate for biomimetic locomotion, and provides robust escape from various local minima traps. Finally, the effectiveness of the proposed method over TAPF are demonstrated through 3D simulations.

The ALA Core Technical Services Workflow Efficiency Interest Group (TSWE IG) held a roundtable discussion at the ALA Annual Conference on 26 June 2022. The themes of the discussion were Diversity, Equity and Inclusion (DEI) cataloging and Integrated Library System (ILS) migration and workflow. The discussions were held at the Washington Convention Center and were led by the two discussion leaders as well as the IG Co-Chairs. The session was well attended, and the discussion was very lively and engaging.

Entity management in a Linked Open Data (LOD) environment is a process of associating a unique, persistent, and dereferenceable Uniform Resource Identifier (URI) with a single entity. It allows data from various sources to be reused and connected to the Web. It can help improve data quality and enable more efficient workflows. This article describes a semi-automated entity management project conducted by the "Wikidata: WikiProject Chinese Culture and Heritage Group," explores the challenges and opportunities in describing Chinese women poets and historical places in Wikidata, the largest crowdsourcing LOD platform in the world, and discusses lessons learned and future opportunities.

Underwater structured light vision systems have attracted the attention of researchers because of their high measurement accuracy and robustness. At present, most underwater structured light vision measurement systems consider the refractive index as a known parameter. However, the refractive index of water will change because of environmental changes such as salinity and temperature. Inaccurate refractive index will have serious influence on the measurement accuracy of the underwater structured light vision system. Therefore, an underwater structured light vision calibration method considering unknown refractive index is proposed. To avoid complex calibration process and complex solution process of a nonlinear underwater structured light vision model, a novel checkerboard usage calibration method based on Aquila Optimizer (AO) is proposed. The calibration solution process is regarded as a nonlinear optimization problem, and the optimization objective is established with the distance between the feature points intersected by the checkerboard and the laser. Finally, calibration and measurement experiments are carried out to verify the effectiveness and superiority of the proposed method.

The accurate detection of faults in robotic fish allows for improving the safety and reliability of its operations. This paper proposes a depth sensor fault diagnosis method based on Gramian Angular Field Fusion and Convolutional Neural Network (GAFF-CNN). Firstly, the depth sensor signals are augmented by a sliding window with overlapping data. Secondly, the one-dimensional time series sensor signals are converted into two-dimensional images by using Gramian Angular Field (GAF). To improve fault diagnosis accuracy and accelerate the training speed, using a weighted fusion method to fuse Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF). After that, the model of CNN is established to train and test fused images for fault diagnosis. The result shows that the fault diagnosis accuracy is the highest at 97.22% when using a weighted coefficient of 0.3, and when the weighted coefficient is 0.4, the training speed is the fastest.

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).

Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

Underwater robot technology has made considerable progress in recent years. However, due to the harsh environment and noise in the flow field near the underwater robots, it is difficult to measure some basic parameters, including swimming speed. The traditional speed measurement methods for underwater robots have the disadvantages of being limited by the environment and bulky. In order to overcome these shortcomings, an artificial lateral line (ALL) sensor based on cantilever structure was developed in this paper. According to the deformation of cantilever beam under water impact, the swimming speed of underwater robots can be measured. In addition, an ‘end-to-end’ calibration algorithm was proposed to calibrate the ALL sensor in the noisy environment, avoiding the complicated noise modeling and filter design process. To reduce the risk of overfitting, a hybrid loss function based on physical model was adopted. Compared with the classical calibration method, our method can reduce the error by 47.8%. Our sensor achieved an average absolute error of 0.07897 m s −1 , and can measure water speed up to 3 m s −1 .

race, they include it “as is”. They do not create definitions. They are up to 139 different terms for race. You can see the terms in a graph on the site. One of the problems is that the records were created by the people in power, and terms were assigned to people. There is a discrepancy between how they define themselves and how they have been defined. This is another reason to send researchers back to the original document.

Inherent bias in Library of Congress Subject Headings (LCSH) and non-LCSHs impedes patron access to materials. To improve discoverability of their juvenile biography collection, librarians at the University of Central Florida Libraries utilized a 39 item Qualtrics questionnaire to analyze the subject headings in the bibliographic records of 952 juvenile biographies. Each biography was evaluated for elements of the biographee's gender, race, ethnicity, and nationality. The resultant data was incorporated into local subject headings yielding 2,972 additional points of discoverability. More inclusive Library of Congress subject headings, and terms from Library of Congress Demographic Group Terms (LCDGT) and Homosaurus were also added.

In the field of nanomanipulation, measurement method for the nano-scale displacement is one of the key technologies, and there are many restrictions on the sensors mounted in microscopic device. In order to reduce the influence of sensors on workspace, we fused the measurements of self-sensing and time-digit-conversion (TDC) method to estimate the measured nanometer displacement. Both of the two methods have a simple measurement circuit and are easy to be integrated. They can reduce the effect of thermal radiation on workspace and work in a vacuum environment (such as SEM chamber). We proposed a method based on Kalman filter with improved state block and neural network to obtain the fusion estimation. Our method achieved a sampling rate equal to that of self-sensing, as well as a precision higher than those of the two source methods. The linearity (R2) of our method is 0.9999915 throughout 8 μ $m$ range. Finally, we compared our method with the traditional fusion method based on statistics.

The teaching programming mode and offline programming mode of welding robot are essential parts in today's manufacturing industry. However, these modes can't meet the automation requirements and adaptive ability of welding robot. To achieve 3D path acquisition of weld seam for robot autonomous programming, a fast and accurate offline 3D seam extraction method is proposed based on monocular structured light sensor. In this method, gray code and phase-shift code raster images are projected to the workpiece, and the 3D coordinates of the weld are calculated by collecting the workpiece images with raster patterns. The path fitting of robot is completed by polynomial fitting method. The novel monocular structured light sensor used by this paper has compact structure and small volume, and can adapt to a variety of welding working scenes. The combination of gray code and phase shift code makes the measurement accurate and fast, and achieves the effect of full resolution decoding. The experimental results show that the proposed method can complete the task of 3D weld extraction and path fitting, which has the characteristics of high efficiency and strong robustness.

In order to solve the vibration problem of low-damping flexible mechanism in motion, a cascade vibration suppression (CVS) trajectory planning method combining the advantages of the optimal double S-curve trajectory planning method and the input shaping method is proposed. The CVS method can effectively suppress the vibration in the period of uniform motion. The correctness of this method is proved by theoretical analysis and digital simulation. Finally, the application results of the proposed method in the FAST feed support system show that it can effectively improve the motion accuracy of the parallel cable mechanism, especially when the motion speed is constant, improving the position tracking accuracy by over 20%.