Search Results (369)

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Straw checkerboard sand barriers play a critical role in wind erosion control and dune stabilization. However, manual installation remains predominant, leading to low efficiency and inconsistent quality. To address this, a compact integrated machine was developed for straw checkerboard laying and pressing using rice straw. The design emphasizes the coordinated function of the straw distribution and pressing systems. Physical parameters of rice straw—average bundle length (<120 cm), repose angle (20.95°), and elastic modulus (1.65 MPa)—were measured to guide structural design. A 3D model of the machine and a multibody dynamic simulation of the distribution system were conducted to validate the mechanical configuration. Field trials were performed using straw mass per metre and average layer thickness as evaluation metrics. Single- and multi-factor experiments combined with response surface methodology yielded optimal parameters: conveyor shaft speed of 230 r/min, crankshaft speed of 227 r/min, and a third-stage tooth height of 0.03 m. Field tests in desert environments confirmed straw output of 0.2–0.4 kg/m, layer thickness of 2–3 cm, burial depth of 14.3–19.5 cm, and exposed height of 19.8–39.5 cm. Results meet quality specifications for barrier construction, demonstrating the machine’s strong applicability and potential for engineering deployment in desertification control. Full article

Controlled thermal stratification in water is crucial for applications such as testing the thermal stealth of underwater vehicles and studying aquatic ecosystems. However, existing laboratory methods for generating such stratified environments often lack stability and uniformity. This study systematically investigates the influence of various hot water injection methods on the stability of thermal stratification. A computational fluid dynamics model, incorporating the overlapping dynamic mesh technique and the Volume of Fluid free-surface capturing method, was used to compare four generation strategies: single-side fixed discharge, towed horizontal discharge, towed vertical upward discharge, and multi-nozzle towed vertical upward discharge. The results indicate that towed discharge methods produce more stable and uniform thermal stratification compared to the fixed discharge method, achieving a 10.1% increase in the water body’s vertical stability coefficient and a 4.5% increase in the Richardson number, while the maximum surface temperature difference was significantly reduced from 0.98 K to 0.37 K. Among the towed methods, vertical upward discharge demonstrated superior stability over horizontal discharge, as it directly transports the high-temperature plume to the upper layer, minimizing thermal exchange with the lower layer, with its vertical stability coefficient and Richardson number being 17.9% and 23% higher, respectively. While maintaining a constant heat input per unit volume, the multi-nozzle configuration yielded ${\mathrm{N}}^2$ and Ri values comparable to the single-nozzle version, while further improving the temperature uniformity at the free surface. Consequently, the towed vertical upward discharge emerges as a highly efficient method for establishing stable and uniform thermal stratification, with the multi-nozzle configuration showing significant promise for large-scale applications. This study provides a valuable reference for creating stratified fluid environments and for related engineering fields. Full article

In the previous studies on the clearance between rotors and stators, only a single physical field or part of the physical fields are considered. In fact, multi-physical fields (inertial moment, temperature, centrifugal, and aerodynamic load) significantly affect the clearance analysis results, especially the inertial load caused by maneuver flight. However, few studies have been found in clearance studies that can comprehensively consider the influence of various loads. To reflect the actual service environment and accurately calculate the clearance, a clearance analysis method is proposed which can consider the inertia moment, temperature, centrifugal, and aerodynamic load simultaneously. Firstly, the dynamic model of the rotor–stator coupled structure is created by using the finite element method. The coupling between blade tenons and disk grooves is realized based on the friction contact way. The bolt connection at the stator flange is simulated by the beam element, and the bearing supports at the rotor are simulated by the spring element. Furthermore, the clearance experiment platform of the rotor–stator coupled structure is constructed, and the validity of the proposed method is verified based on the clearance test results. Finally, the influence of multi-physical fields on the clearance of the rotor–stator coupled structure is investigated, and some new clearance variation rules are found. Full article

Extended-reach drilling (ERD) offers substantial economic and operational benefits by accessing extensive reservoir sections with fewer surface facilities, yet poses significant frictional challenges due to complex wellbore geometries and extreme operating conditions. This study introduces a multi-interface lubrication evaluation framework. It systematically assesses oil-based drilling fluids (OBDFs) across three downhole contact scenarios: metal–rock, metal–mud cake, and metal–metal interfaces under HTHP conditions. We developed a quantitative, normalized scoring system. Benchmarked against distilled water (score 0) and W1-110 mineral oil (score 100), it integrates frictional data from various tests into a unified metric for lubricant comparison. Three candidate lubricants—PF-LUBE EP, PF-LUBE OB, and CX-300—were evaluated at varying dosages, lithologies, and applied loads. Results show that at 2 wt%, PF-LUBE EP achieved the most consistent performance, reducing friction coefficients by 36.8% (metal–rock), 27.5% (metal–mud cake), and 32.5% (metal–metal), with a normalized average score of 155.39, outperforming PF-LUBE OB and CX-300 by 12.5% and 18.3%, respectively. Its superior performance is attributed to a bionic dual-layer film formed by organophosphorus anchoring and alkyl slip layers, enabling self-healing and stability under cyclic loading and HTHP environments. PF-LUBE OB and CX-300 also demonstrated friction reduction but with lower normalized scores (138.06 and 131.27), reflecting less stability across varied conditions. The proposed framework bridges the gap between laboratory testing and field-scale application by capturing multi-interface behaviors, enabling objective lubricant selection and dosage optimization for complex ERD operations. These findings not only validate PF-LUBE EP as a robust additive but also establish a scalable methodology for the development and optimization of next-generation OBDF formulations aimed at reducing torque, drag, and equipment wear in challenging drilling environments. Full article

To address the challenges of detecting dynamic small targets such as pedestrians in complex dynamic environments for mobile robots, this paper proposes a dynamic small-target detection algorithm based on feature fusion and rediffusion structure, which is suitable for deployment on mobile robot platforms. Mobile robots can utilize depth camera information to identify and avoid small targets like pedestrians and vehicles in complex environments. Traditional deep learning-based object detection algorithms perform poorly when applied to the field of mobile robotics, especially in detecting dynamic small targets. To improve this, we apply an enhanced object recognition algorithm to mobile robot platforms. To verify the effectiveness of the proposed algorithm, we conduct relevant tests and ablation studies in various environments and perform multi-class small-target detection on the public VisDrone2019 dataset. Compared with the original YOLOv8 algorithm, our proposed method improves accuracy by 5% and increases mAP0.5 and mAP0.5–0.95 by approximately 3%. Overall, the experimental results show that the high-performance small-target detection algorithm based on feature fusion and rediffusion structure significantly reduces the miss detection rate and exhibits good generalization ability, which can be extended to multi-class small-target detection. This is of great significance for improving the environmental perception ability of robots. Full article

Semi-enclosed bays are greatly affected by human activities and have undergone drastic changes in their ecological environment, which requires our continuous attention. Laizhou Bay (LZB) is a typical semi-closed bay that is greatly influenced by human activities, and it is highly representative on a global scale. Investigating the multi-scale variation in nutrient concentrations in semi-enclosed bays can provide scientific support for environmental management and policy adjustments. To address the limitations of in situ data and the high cost of field surveys, this study utilizes machine learning methods to construct MODIS remote sensing models for quantitatively analyzing the concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the surface water of LZB, as well as the spatiotemporal factors influencing them. Among various methods tested, the Support Vector Machine Regression (SVR) algorithm demonstrated the best performance in retrieving nutrient concentrations in LZB. The R² values of the DIN and DIP retrieval results based on the SVR algorithm are 0.91 and 0.92, respectively, while the RMSE values are 5.43 and 0.08 μmol/L, respectively. The retrieval results indicate that nearshore nutrient concentrations are significantly higher than those in offshore areas. Temporally, from 2003 to 2024, the DIN concentration in l has decreased at a rate of 0.4 μmol/L/yr, while the DIP concentration has remained relatively stable. Given sufficient observation data, the proposed machine learning and remote sensing approach can be effectively applied to other bays, offering the advantages of long time series, high spatial resolution, and a low cost. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

Metaheuristic algorithms are widely used for solving complex optimization problems without relying on gradient information. They efficiently explore large, non-convex, and high-dimensional search spaces but face challenges with dynamic environments, multi-objective goals, and complex constraints. This paper introduces a novel hybrid algorithm, Fick’s Law Algorithm with Opposition-Based Learning (FLA-OBL), combining the FLA’s strong exploration–exploitation balance with OBL’s enhanced solution search. Tested on CEC2017 benchmark functions, FLA-OBL outperformed state-of-the-art algorithms, including the original FLA, in convergence speed and solution accuracy. To address real-world multi-objective problems, we developed FFLA-OBL (Fuzzy FLA-OBL) by integrating a fuzzy logic system for UAV path planning with obstacle avoidance. This variant effectively balances exploration and exploitation in complex, dynamic environments, providing efficient, feasible solutions in real time. The experimental results confirm FFLA-OBL’s superiority over the original FLA in both solution optimality and computational efficiency, demonstrating its practical applicability for multi-objective optimization in UAV navigation and related fields. Full article

Rolling Bearings are crucial components for induction motors and generators in electric vehicles (EVs), as their performance considerably influences the system’s operational reliability and safety. However, the commercial greases used for bearing lubrication in EV motors pose a detrimental impact on the environment. In addition, they are ineffective in mitigating the effect of electric discharges on rolling surfaces leading to premature bearing failures. This study investigates the viability of a developed eco-friendly grease from palm olein as the base oil and glycerol monostearate as the thickener, enhanced with conductive multi-walled carbon nanotubes (MWCNTs) for EV motor bearings prone to electrical currents. Chemical–physical, tribological, and electrical tests were conducted on the developed grease samples without and with MWCNTs at 1 wt.%, 2 wt.%. and 3 wt.% concentrations and results were compared to lithium and sodium greases. Palm grease samples demonstrated a lower EDM voltage range reaching 1.0–2.2 V in case of 3 wt.% MWCNTs blends, indicating better electrical conductivity and protecting the bearing surfaces from electric-related faults. These findings were further confirmed using vibrations measurement and SEM-EDX analysis of the electrically worn bearings. Bearings lubricated with palm grease blends exhibited lower vibration levels. Palm grease with 2 wt.% MWCNTs reduced vibration amplitudes by 28.4% (vertical) and 32.3% (horizontal). Analysis of bearing damaged surfaces revealed enhanced damaged surface morphology for MWCNT-enhanced palm grease as compared to surface lubricated by commercial greases. The results of this work indicate that the proposed bio-grease is a promising candidate for future application in the field of next-generation electric mobility systems. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article

With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field. To address the above challenges, this paper proposes a blockchain-based data transaction and privacy protection framework. First, the framework builds a multi-layer security architecture that integrates blockchain and IPFS and adapts to the “end–edge–cloud” collaborative characteristics of IoT. Secondly, a data sharing mechanism that takes into account both access control and interest balance is designed. On the one hand, the mechanism uses attribute-based encryption (ABE) technology to achieve dynamic and fine-grained access control for massive heterogeneous IoT devices; on the other hand, it introduces a game theory-driven dynamic pricing model to effectively balance the interests of both data supply and demand. Finally, in response to the needs of confidential analysis of IoT data, a secure computing scheme based on CKKS fully homomorphic encryption is proposed, which supports efficient statistical analysis of encrypted sensor data without leaking privacy. Security analysis and experimental results show that this scheme is secure under standard cryptographic assumptions and can effectively resist common attacks in the IoT environment. Prototype system testing verifies the functional completeness and performance feasibility of the scheme, providing a complete and effective technical solution to address the challenges of data integrity, verifiable transactions, and fine-grained access control, while mitigating the reliance on a trusted central authority in IoT data sharing. Full article