Search Results (1,124)

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments. Full article

Liquid crystal elastomers (LCEs) have shown great potential in the field of soft robotics due to their unique actuation capabilities. Despite the growing number of experimental studies in the soft robotics field, theoretical research remains limited. In this paper, a dynamic model of a bionic arm using an LCE fiber as artificial muscle is established, which exhibits periodic oscillation controlled by periodic illumination. Based on the assumption of linear damping and angular momentum theorem, the dynamics equation of the model oscillation is derived. Then, based on the assumption of linear elasticity model, the periodic spring force of the fiber is given. Subsequently, the evolution equations for the cis number fraction within the fiber are developed, and consequently, the analytical solution for the light-excited strain is derived. Following that, the dynamics equation is numerically solved, and the mechanism of the controllable oscillation is elucidated. Numerical calculations show that the stable oscillation period of the bionic arm depends on the illumination period. When the illumination period aligns with the natural period of the bionic arm, the resonance is formed and the amplitude is the largest. Additionally, the effects of various parameters on forced oscillation are analyzed. The results of numerical studies on the bionic arm can provide theoretical support for the design of micro-machines, bionic devices, soft robots, biomedical devices, and energy harvesters. Full article

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

The paper presents a shared control strategy that allows a human operator to physically guide the end-effector of a robotic manipulator to perform different tasks, possibly in interaction with the environment. To switch among different operational modes referring to a finite state machine algorithm, ElectroMyoGraphic (EMG) signals from the user’s arm are used to detect muscular contractions and to interact with a variable admittance control strategy. Specifically, a Support Vector Machine (SVM) classifier processes the raw EMG data to identify three classes of contractions that trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco² manipulator, equipped with force/torque sensor at the end-effector, and with a limited group of users wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb, demonstrating promising results. Full article

In complex dynamic environments, robotic arms face multiple challenges such as real-time environmental changes, high-dimensional state spaces, and strong uncertainties. Trajectory planning tasks based on deep reinforcement learning (DRL) suffer from difficulties in acquiring human expert strategies, low experience utilization (leading to slow convergence), and unreasonable reward function design. To address these issues, this paper designs a neural network-based expert-guided triple experience replay mechanism (NETM) and proposes an improved reward function adapted to dynamic environments. This replay mechanism integrates imitation learning’s fast data fitting with DRL’s self-optimization to expand limited expert demonstrations and algorithm-generated successes into optimized expert experiences. Experimental results show the expanded expert experience accelerates convergence: in dynamic scenarios, NETM boosts accuracy by over 30% and safe rate by 2.28% compared to baseline algorithms. Full article

Sulfur particle deposition and wellbore blockage significantly hinder the productivity of high-sulfur gas wells, necessitating accurate prediction of the critical gas flow velocity to prevent deposition. This study presents a comprehensive force-based model to determine the critical gas flow velocity in vertical wells, explicitly incorporating adhesion, boundary layer effects, and particle detachment mechanisms. Through detailed analysis, the forces acting on sulfur particles of varying sizes and flow velocities, as well as the key factors influencing the critical gas flow velocity, were examined. The results demonstrated strong agreement with the experimental data, with a mean absolute percentage error of 6%, while revealing significant deviations from the conventional critical gas suspension velocity, validating the model’s enhanced accuracy and its necessity. This study identified adhesive forces as dominant for small particles (<100 µm) at low velocities (≤10 m/s), whereas gravitational and inertial forces prevailed for larger particles. Key parameters such as the particle size, sphericity, Hamaker constant, friction coefficient, and rolling arm length ratio critically influenced the deposition velocity and detachment mechanisms. These findings provide fundamental insights into sulfur deposition dynamics and establish a scientific basis for optimizing wellbore operations to mitigate sulfur accumulation and improve production efficiency in high-sulfur gas wells. Full article

This paper presents a novel interface that leverages a smartwatch for controlling industrial robotic arms. By harnessing the gyroscope and advanced gesture recognition capabilities of the smartwatch, our solution facilitates intuitive, real-time manipulation that caters to users ranging from novices to seasoned professionals. A dedicated application is implemented to aggregate sensor data via an open-source library, providing a streamlined alternative to conventional control systems. The experimental setup consists of a smartwatch equipped with a data collection application, a robotic arm, and a communication module programmed in Python. Our aim is to evaluate the practicality and effectiveness of smartwatch-based control in a real-world industrial context. The experimental results indicate that this approach significantly enhances accessibility while concurrently minimizing the complexity typically associated with automation systems. Full article

This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and traditional Structure-from-Motion (SfM) photogrammetry. The research targets artifacts smaller than 5 cm, characterized by complex geometries and reflective surfaces that pose challenges for conventional recording methods. To address the limitations of traditional methods without resorting to the high costs associated with laser scanning, this study explores NeRF and GS as cost-effective and efficient alternatives. A comprehensive experimental framework was established, incorporating ground-truth data obtained using a metrological articulated arm and a rigorous quantitative evaluation based on root mean square (RMS) error, Chamfer distance, and point cloud density. The results indicate that while NeRF outperforms GS in terms of geometric fidelity, both techniques still exhibit lower accuracy compared to SfM, particularly in preserving fine geometric details. Nonetheless, NeRF demonstrates strong potential for rapid, high-quality 3D documentation suitable for visualization and dissemination purposes in cultural heritage. These findings highlight both the current capabilities and limitations of neural rendering techniques for archaeological documentation and suggest promising future research directions combining AI-based models with traditional photogrammetric pipelines. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

The design of the suspension system affects handling and stability, vibrations of the steered wheels, vehicle ride comfort, and tyre tread wear. One of the most important vibration parameters is acceleration; high acceleration values can have an adverse effect on both the driver and passengers, as well as on the components of the vehicle’s suspension and handling. This paper presents the results of the effects of acceleration on the components of a front-independent MacPherson suspension system. Data on the accelerations were obtained from theoretical and experimental studies. A simulation study was conducted, taking into account the elastic and damping characteristics of the elastic components. The experimental study was conducted under laboratory conditions by using a suspension tester, BEISSBARTH, and a measuring system developed with LabVIEW 2021 SP1 and MATLAB R2022b software. The experiments were conducted with different tyre pressures and by using bushings made from different materials. The experimental tests were conducted with two rubber bushings within the mounting of the arm, as well as a rubber bushing and a polyurethane bushing. The experimental results were compared and analyzed. Two theoretical models were considered: one is a mathematical model, and the other is a simulation model which uses the finite element method. Numerical dynamic analysis of the suspension was performed using the SolidWorks 2023. Full article

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. Full article

Circulating current control is a critical part of the Modular Multilevel Converter (MMC) control system. Existing control methods rely on arm current information obtained from complex current measurement devices. However, these devices are susceptible to failures, which can lead to distorted arm currents, increased peak arm current values, and higher losses. In extreme cases, this can result in system instability. This paper first analyzes four typical arm current measurement faults, i.e., constant gain faults, amplitude deviation faults, phase shift faults, and stuck faults. Then, a Kalman Filter (KF)-based fault detection method is proposed, which allows for the simultaneous monitoring status of all six arm current measurements. Moreover, to facilitate fault detection, the Moving Root Mean Square (MRMS) value of the observation residual is defined, which effectively detects faults while suppressing noise. The entire fault detection process takes less than 20 ms. Finally, the feasibility and effectiveness of the proposed method are validated through MATLAB/Simulink simulations and experimental results. Full article