Search Results (407)

The miniaturization of ocean parameter monitors is critical for environmental monitoring and oceanographic research. In recent years, rapid developments in data processing, artificial intelligence, micro-nano manufacturing and advanced materials have significantly improved sensing accuracy while reducing device size. The detection of key ocean parameters such as temperature, salinity, pressure, dissolved oxygen (DO), pH, nutrients, chlorophyll and so on is facilitated by these innovations. Among these emerging technologies, microfluidics and optofluidics have attracted large attention in the fields of biomedicine and environmental monitoring. These platforms have the advantages of high sensitivity, low power consumption and easy integration. Real-time and on-site monitoring can be achieved by them. Optofluidic technology shows great prospects for ocean sensing applications. Recent advances in optofluidic ocean sensors for the measurement of chemical parameters and their future potential are highlighted in this review. Ultimately, it presents the key role of optofluidic systems in realizing compact high-performance ocean parameter sensors. This plays an important role in paving the way for their integration into micro robots and the fourth generation of submersibles based on live fish. Full article

With the rise of embodied agents and indoor service robots, object detection has become a critical component supporting semantic mapping, path planning, and human–robot interaction. However, indoor scenes often face challenges such as severe occlusion, large-scale variations, small and densely packed objects, and complex textures, making existing methods struggle in terms of both robustness and accuracy. This paper proposes MDF-YOLO, a multi-domain fusion detection framework based on Hölder regularity guidance. In the backbone, neck, and feature recovery stages, the framework introduces the CrossGrid Memory Block, Hölder-Based Regularity Guidance–Hierarchical Context Aggregation module, and Frequency-Guided Residual Block, achieving complementary feature modeling across the state space, spatial domain, and frequency domain. In particular, the HG-HCA module uses the Hölder regularity map as a guiding signal to balance the dynamic equilibrium between the macro and micro paths, thus achieving adaptive coordination between global consistency and local discriminability. Experimental results show that MDF-YOLO significantly outperforms mainstream detectors in metrics such as mAP@0.5, mAP@0.75, and mAP@0.5:0.95, achieving values of 0.7158, 0.6117, and 0.5814, respectively, while maintaining near real-time inference efficiency in terms of FPS and latency. Ablation studies further validate the independent and synergistic contributions of CGMB, HG-HCA, and FGRB in improving small-object detection, occlusion handling, and cross-scale robustness. This study demonstrates the potential of Hölder regularity and multi-domain fusion modeling in object detection, offering new insights for efficient visual modeling in complex indoor environments. Full article

Minimally invasive surgery (MIS) has become increasingly favored by both patients and surgeons owing to its advantages such as shortened recovery times and reduced surgical trauma. To enhance intraoperative feedback from surgical instruments while minimizing harmful radiation exposure, a wide range of electromagnetic tracking systems (EMTS) has been developed at micro scales for medical applications. This review provides a comprehensive summary of advances in the field over the past five years, with an emphasis on the working principles of EMTS, system architecture, current research progress, and clinical applications. In comparison to other review papers, this article focuses specifically on EMTS for medical micro-devices, such as robotic catheters, endoscopes, and capsule robots. Moreover, Representative research studies and commercial systems are presented along with their clinical implementations, placing greater emphasis on the translation of EMTS into medical applications. Finally, this review outlines and discusses future research directions, highlighting major challenges and potential opportunities for advancing the integration of EMTS into routine clinical workflows. Full article

Testicular stiffness is a potential indicator of spermatogenic activity. Herein, we investigated the relationship between testicular stiffness and intratesticular morphology in Syrian hamsters by using a robotic system with a micro-force sensor. Animals were divided into control, sham-operated, and surgically induced cryptorchidism groups. Testicular stiffness, testis weight and size, and Johnsen score data for sham and crypt groups were partially derived from our previous study and reanalysed. Testicular stiffness and histological parameters were analysed, including tunica albuginea thickness, seminiferous tubule occupancy, tubule diameter, intratubular cell-layer thickness, peritubular lamina propria thickness, and Leydig cell numbers. Compared with those of sham and normal controls, cryptorchid testes showed significantly lower stiffness and marked morphological changes, such as reduced tubule occupancy and diameter, thinner intratubular cell layers, thickened tunica albuginea and peritubular lamina propria, and increased numbers of Leydig cells. Decreased testicular stiffness and the Johnsen score, a standard index of spermatogenic function, were strongly related to these structural changes. These findings indicate that structural changes in the testes caused by impaired spermatogenesis are related to measurable differences in tissue stiffness. This study supports using mechanical properties as non-invasive quantitative indices to evaluate testicular function in animal models, offering a novel approach for future research in experimental andrology. Full article

Robotisation is playing an increasingly important role in economic and technological life today. Industrial robotisation has a significant impact on the efficiency and productivity of manufacturing companies, and service robots are becoming more and more common in everyday life. The main objective of our research is to examine the impact of robotisation on energy consumption and sustainability, as well as the technological and corporate challenges facing the integration of robots. The research is based on a literature review, which we supplemented with a bibliographic analysis. In terms of methods, we relied on the Global Citation Score, Co-Coupling Network Analysis, and Burst Analysis. Our results suggest that research on industrial robotisation can be divided into complementary dimensions, ranging from engineering-level trajectory optimization and subsystem design to system-level modeling, macroeconomic sustainability analysis, and data-driven optimization. The findings highlight that the positive impacts of robotisation on both energy efficiency and carbon reduction can be maximized when these approaches are integrated into a systemic framework that connects micro- and macro-level perspectives. Full article

With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to address labor shortages. In this context, this paper presents a hierarchical and distributed control system based on ROS 2 and Micro-ROS. The distributed architecture decouples functional modules, improving system maintainability and supporting modular upgrades. The control system consists of a three-layer structure, including a high-level controller, Jetson Nano, for gait data processing and advanced command generation; a middle-layer controller, ESP32-S3, for sensor data fusion and inter-layer communication bridging; and a low-level controller, STM32F405, for field-oriented control to drive the motors along a predefined trajectory. Experimental validation in both early and late rehabilitation stages demonstrates the system’s ability to achieve accurate trajectory tracking. In the early rehabilitation stage, the maximum root mean square error of the joint motors is 1.143°; in the later rehabilitation stage, the maximum root mean square error of the joint motors is 1.833°, confirming the robustness of the control system. Additionally, the hierarchical and distributed architecture ensures maintainability and facilitates future upgrades. This paper provides a feasible control scheme for the next generation of lower limb rehabilitation robots. Full article

This paper introduces a variant of differential evolution with micro-populations, called $\mu$-DE-ERM, which incorporates a periodic elitist replacement mechanism with the aim of preserving diversity without the need to measure it explicitly. The proposed algorithm is designed for scenarios with reduced evaluation budgets, where efficiency and convergence stability are critical. Its performance is evaluated on CEC 2005 and CEC 2017 benchmark suites, covering unimodal, multimodal, hybrid, and composition functions, as well as on two real-world engineering problems: the identification of dynamic parameters and the tuning of a PID controller for a one-degree-of-freedom robotic manipulator. The comparative analysis shows that $\mu$-DE-ERM achieves competitive or superior results against its predecessors DE and $\mu$-DE, and remains effective when contrasted with advanced algorithms such as L-SHADE and RuGA. Furthermore, additional comparisons with algorithms with competitive replacement mechanisms, $\mu$-DE-Cauchy and $\mu$-DE-Shrink, confirm the robustness of the proposal in real applications, particularly under strict computational constraints. These findings support $\mu$-DE-ERM as a practical and efficient alternative for optimization problems in resource-limited environments, delivering reliable solutions at low computational cost. Full article

Structural applications that use High-Strength Low-Alloy (HSLA) steels require detailed microstructural analysis to manufacture welded components that combine strength and weldability. The balance of these properties depends on both the chemical composition and the welding parameters. Moreover, in multi-pass welds, thermal cycling results in a complex Heat-Affected Zone (HAZ), characterized by sub-regions with a multitude of microstructural constituents, including brittle phases. This study investigates the influence of Vanadium addition on the microstructure and performance of the HAZ. Multi-pass welded joints were manufactured on 15 mm thick S355 steels with different Vanadium contents using a robotic GMAW process. A steel variant containing both Vanadium and Niobium was also considered, and the results were compared to those of standard S355 steel. Moving through the different sub-regions of the welded joints, the results show a heterogeneous microstructure characterized by ferrite, bainite and martensite/austenite (M/A) islands. The presence of Vanadium reduces carbon solubility during the phase transformations involved in the welding process. This results in the formation of very fine (average size 11 ± 4 nm) and dispersed precipitates, as well as a lower percentage of the brittle M/A phase, in the variant with a high Vanadium content (0.1 wt.%), compared to the standard S355 steel. Despite the presence of the brittle phase, the micro-alloyed variants exhibit strengthening without loss of ductility. The combined presence of both hard and soft phases in the HAZ provides stress-damping behavior, which, together with the very fine precipitates, promises improved resistance to crack propagation under different loading conditions. Full article

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

Nano- and micro-indentation have become essential tools for quantifying the micromechanical behavior of rocks beyond traditional macroscopic tests. This review summarizes the historical evolution, experimental methodologies, and interpretation models (e.g., Oliver–Pharr, Doerner–Nix, energy-based methods, Hertz/ECM/Lawn), with a particular focus on rock-specific challenges such as heterogeneity, anisotropy, and surface roughness. A structured literature survey (1980–August 2025) covers representative studies on shale, limestone, marble, sandstone, claystone, and granite. The transition from classical hardness measurements to advanced instrumented indentation has enabled more reliable determination of localized properties, including hardness, elastic modulus, fracture toughness, and creep. Special attention is given to the applicability and limitations of different interpretation models when applied to heterogeneous and anisotropic rocks. Current challenges include high sensitivity to surface conditions and difficulties in capturing the full complexity of natural rock behavior. Looking forward, promising directions involve intelligent systems that integrate AI-driven data analytics, robotic automation, and multiscale modeling (from molecular dynamics to continuum FEM) to enable predictive material design. This review aims to provide geoscientists and engineers with a comprehensive foundation for the effective application and further development of indentation-based testing in rock mechanics and geotechnical engineering. Full article

Robotic surgery has become a cornerstone of modern urologic practice, with the da Vinci system maintaining dominance for over two decades. In recent years, however, a new generation of robotic platforms has emerged, introducing greater competition and innovation into the field. These systems aim to address unmet needs through features such as modular architectures, enhanced ergonomics, haptic feedback, and cost-containment strategies. Several platforms—including Hugo™ RAS, Versius™, Avatera™, REVO-I, Hinotori™, Senhance™, KangDuo, MicroHand S, Dexter™, and Toumai^®—have entered clinical use with early results demonstrating perioperative and short-term oncologic outcomes broadly comparable to those of established systems, particularly in procedures such as radical prostatectomy, partial nephrectomy, and radical cystectomy. At the same time, they introduce unique advantages in workflow flexibility, portability, and economic feasibility. Nevertheless, important challenges remain, including the need for rigorous comparative trials, standardized training curricula, and long-term cost-effectiveness analyses. The integration of artificial intelligence, augmented reality, and telesurgery holds the potential to further expand the role of robotics in urology, offering opportunities to enhance precision, improve accessibility, and redefine perioperative care models. This review summarizes the evolving landscape of robotic platforms in urology, highlights their clinical applications and limitations, and outlines future directions for research, training, and global implementation. Full article

The art of folding paper, named “origami”, has transformed from serving religious and cultural purposes to various educational and entertainment purposes in the modern world. Significantly, the fundamental folds and creases in origami, which enable the creation of 3D structures from a simple flat sheet with unique crease patterns, serve as a great inspiration in engineering applications such as deployable mechanisms for space exploration, self-folding structures for exoskeletons and surgical procedures, micro-grippers, energy absorption, and programmable robotic morphologies. Therefore, this paper will provide a systematic review of the state-of-the-art origami-inspired structures that have been adopted and exploited in robotics design and operation, called origami-inspired robots (OIRs). The advantages of the flexibility and adaptability of these folding mechanisms enable robots to achieve agile mobility and shape-shifting capabilities that are suited to diverse tasks. Furthermore, the inherent compliance structure, meaning that stiffness can be tuned from rigid to soft with different folding states, allows these robots to perform versatile functions, ranging from soft interactions to robust manipulation and a high-DOF system. In addition, the potential to simplify the fabrication and assembly processes, together with its integration into a wide range of actuation systems, further broadens its capabilities. However, these mechanisms increase the complexity in theoretical analysis and modelling, as well as posing a challenge in control algorithms when the robot’s DOF and reconfigurations are significantly increased. By leveraging the principles of folding and integrating actuation and design strategies, these robots can adapt their shapes, stiffness, and functionality to meet the demands of diverse tasks and environments, offering significant advantages over traditional rigid robots. Full article

The aim of the present work was to select the thickness of a silicone layer coating a PES woven fabric designed for application in the artificial transverse muscles as a component of the medical robot. The artificial muscle in the form of U-shaped tube is subjected to repeated bending. Due to this fact, an important task was to ensure fatigue resistance to the bending of the muscle component. The fatigue bending was performed using the STM 601/12 device manufactured by SATRA Technology, Northamptonshire, UK. The surface geometry of the fabric before and after coating, as well as after 4000, 10,000 and 20,000 cycles of bending, was assessed using the MicroSpy^® Profile profilometer manufactured by the FRT, the art of metrology, Bergisch Gladbach, Germany. Additionally, the microscopic observations of fabric surface were performed after the abovementioned cycles of fatigue bending. The results obtained showed that in order to ensure the required functionality of the coated fabric, the 0.2 mm silicone layer is better than the 0.1 mm silicone layer. Full article

Rapid developments in intelligent interfaces across service, healthcare, and industry have led to unprecedented demands for advanced tactile perception systems. Traditional tactile sensors often struggle with adaptability on curved surfaces and lack sufficient feedback for delicate interactions. Flexible and wearable tactile sensors are emerging as a revolutionary solution, driven by innovations in flexible electronics and micro-engineered materials. This paper reviews recent advancements in flexible tactile sensors, focusing on their mechanisms, multifunctional performance and applications in health monitoring, human–machine interactions, and robotics. The first section outlines the primary transduction mechanisms of piezoresistive (resistance changes), capacitive (capacitance changes), piezoelectric (piezoelectric effect), and triboelectric (contact electrification) sensors while examining material selection strategies for performance optimization. Next, we explore the structural design of multifunctional flexible tactile sensors and highlight potential applications in motion detection and wearable systems. Finally, a detailed discussion covers specific applications of these sensors in health monitoring, human–machine interactions, and robotics. This review examines their promising prospects across various fields, including medical care, virtual reality, precision agriculture, and ocean monitoring. Full article

Industry 4.0 (I4.0) is reshaping manufacturing by integrating advanced digital technologies and is increasingly seen as an enabler of the circular economy (CE). However, most research treats digitalisation and circularity separately, with limited empirical insight regarding their combined implementation. This study investigates I4.0 adoption to support sustainability and CE across industries, focusing on how enterprise size influences adoption patterns. Based on survey data from 69 enterprises, the research examines which technologies are applied, at what stages of the product life cycle, and what barriers and drivers influence uptake. Findings reveal a modest but growing adoption led by the Internet of Things (IoT), big data, and integrated systems. While larger firms implement more advanced tools (e.g., robotics and simulation), smaller enterprises favour accessible solutions (e.g., IoT and cloud computing). A positive link is observed between digital adoption and CE practices, though barriers remain significant. Five main categories of perceived obstacles are identified: political/institutional, financial, social/market-related, technological/infrastructural, and legal/regulatory. Attitudinal resistance, particularly in micro and small enterprises, emerges as an additional challenge. Based on these insights, and to support the twin transition, the paper proposes targeted policies, including expanded funding, streamlined procedures, enhanced training, and tools for circular performance monitoring. Full article