Search Results (82)

Time-of-flight sensors have emerged as a viable solution for real-time distance sensing in robotic safety applications due to their compact size, fast response, and contactless operation. This study addresses one of the key challenges with time-of-flight sensors, focusing on how they perceive and evaluate the environment, particularly in the presence of complex geometries and reflective surfaces. Using a Universal Robots UR5e arm in a controlled indoor workspace, two different sensors were tested across eight scenarios involving objects of varying shapes, sizes, materials, and reflectivity. Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. Results show that the sensor’s field of view and operating principle significantly affect its spatial resolution and object boundary detection, with narrower fields of view providing more precise measurements and wider fields of view demonstrating greater resilience to specular reflections. These findings offer valuable insights into selecting appropriate ToF sensors for integration into robotic safety systems, particularly in environments with reflective surfaces and complex geometries. Full article

Soil bacterial community composition and diversity can be an important bioindicator for assessing ecosystem stability, and photovoltaic (PV) shading is a key factor influencing soil bacterial communities in rocky desertification areas; however, how the composition and diversity of soil bacterial communities change with PV operation duration remains unclear. Focusing on the experimental demonstration site of Shilin ecological photovoltaic (PV) power plant in Yunnan Province, we compared soil properties under PV arrays and non-PV control areas with different operation durations (7 and 13 years). The results showed that long-term PV operation significantly increased soil TN and TK content compared to CK, while increasing Ure and ALP activities, but inhibiting CAT activity and decreasing soil moisture, pH, SOC, and TP. High-throughput sequencing revealed stable dominant bacterial phyla (e.g., Aspergillus, Acidobacteriota) and beneficial genera (e.g., RB41, Sphingomonas), with an increase in relative abundance of Bacillota-like phyla but a decrease in Acidobacterium. The α-diversity (ACE, Chao1 index) and β-diversity of soil bacteria greatly increased with years of PV operation, reaching a maximum in the 13-year PV operation area. Correlation analyses showed that differences in soil bacterial communities in regions with different years of PV operation were mainly influenced by differences in PH and enzyme activities. Full article

Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article

In this paper, a specific parametric and open-source algorithm for the direct and inverse kinematics of the UR5e Cobot is formulated by using the (n, o, a, p) transformation matrix, along with the inverse matrices, and then implemented in Matlab for numerical validation purposes. Thus, a specific robotized cell that includes novel mechatronic devices has been designed and built at LARM (Lab. of Robotics and Mechatronics) in Cassino in order to experimentally validate the proposed algorithm. In particular, many experimental points to carry out the whole automatic cycle have been detected by using the corresponding teach-pendant tool and joint positions for different UR5e Cobot poses. In addition, this consistent experimental campaign has allowed to evaluate the percentage accuracy of the robot, which can be useful for the practical applications. Therefore, the proposed kinematic model, along with the parametric and open-source algorithm, of the UR5e Cobot can be useful to simulate different applications in several robotized cells with a good reliability with respect to the real program of the robot. Full article

Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu’s thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. A $6\times 6$ Dragon Skin-based sensor array was fabricated, embedded with copper yarn electrodes, and evaluated using a UR3e robotic arm and a Schunk force-torque sensor to generate controlled stimuli. The fabricated tactile sensor measures the applied force from 1 to 3 N. Sensor output was captured via a MUCA breakout board and Arduino Nano 33 IoT, transmitting the Ratio of Mutual Capacitance data for further analysis. A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. Decision Tree Regression achieved the highest performance ($R^2=0.9996, RMSE=0.0446$), providing an effective correlation factor of $51.76$ for force estimation. The system offers robust performance in complex interactions and a scalable solution for soft robotics and prosthetic force mapping, supporting health monitoring, safe automation, and medical diagnostics. Full article

High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness. Full article

This paper presents a data-driven kinematic model for the end-effector pose control applied to a variety of manipulator robots, focusing on the entire end-effector’s pose (position and orientation). The measured signals of the full pose and their computed derivatives, along with a linear combination of an estimated Jacobian matrix and a vector of joint velocities, generate a model estimation error. The Jacobian matrix is estimated using the Pseudo Jacobian Matrix (PJM) algorithm, which requires tuning only the step and weight parameters that scale the convergence of the model estimation error. The proposed control law is derived in two stages: the first one is part of an objective function minimization, and the second one is a constraint in a quasi-Lagrangian function. The control design parameters guarantee the control error convergence in a closed-loop configuration with adaptive behavior in terms of the dynamics of the estimated Jacobian matrix. The novelty of the approach lies in its ability to achieve superior tracking performance across different manipulator robots, validated through simulations. Quantitative results show that, compared to a classical inverse-kinematics approach, the proposed method achieves rapid convergence of performance indices (e.g., Root Mean Square Error (RMSE) reduced to near-zero in two cycles vs. a steady-state RMSE of 20 in the classical approach). Additionally, the proposed method minimizes joint drift, maintaining an RMSE of approximately 0.3 compared to 1.5 under the classical scheme. The control was validated by means of simulations featuring an UR5e manipulator with six Degrees of Freedom (DOF), a KUKA Youbot with eight DOF, and a KUKA Youbot Dual with thirteen DOF. The stability analysis of the closed-loop controller is demonstrated by means of the Lyapunov stability conditions. Full article

Background: Osteoporosis is a metabolic bone disease with a high mortality rate due to non-traumatic fractures. The risk of osteoporosis is increasing globally due to an increasing aging population. Current therapies are limited to delaying disease progression. Recently, the need to discover foods with osteogenic activity for the prevention and treatment of osteoporosis has been emphasized. We focused on bone formation via osteoblast differentiation, considering bone formation and resorption during bone homeostasis. Rubus tozawae Nakai ex J. Y. Yang (RL, Geoje raspberry) is a deciduous subshrub that has been traditionally eaten for its fruit. Methods and Results: We identified the third subfraction of n-hexane fraction (RL-Hex-NF3) of RL, an endemic Korean plant with osteogenic activity, which increased bone density in ovariectomized mice, a representative animal model of osteoporosis, via the depletion of female hormones, which resulted from the increase in the osteoblast population. RL-Hex-NF3 induced osteoblast differentiation and the expression of osteogenic markers in MC3T3-E1 pre-osteoblasts. Seven compounds were identified from RL-Hex-NF3 using NMR spectroscopy. Of these, three compounds, namely, 3β-hydroxy-18α,19α-urs-20-en-28-oic acid, betulinic acid, and (1S,6R,7S)-muurola-4,10(14)-diene-15-ol, showed strong osteogenic activity. Conclusions: RL-Hex-NF3 and its compounds suppress bone loss via their osteogenic properties, suggesting that they could be a potent candidate to treat osteoporosis. Full article

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system’s ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction. Full article

Citrobacter freundii is an opportunistic pathogen of freshwater aquatic animals, which severely restricts the sustainable development of the aquaculture industry. In this study, a dominant strain, named FSNM-1, was isolated from the hepatopancreas of diseased Macrobrachium rosenbergii. This strain was identified as C. freundii based on a comprehensive analysis of its morphological, physiological, and biochemical features and molecular identification. Challenge experiments were conducted to assess the pathogenicity of C. freundii to M. rosenbergii. The results showed that the FSNM-1 strain had high virulence to M. rosenbergii with a median lethal dose (LD₅₀) of 1.1 × 10⁶ CFU/mL. Histopathological analysis revealed that C. freundii infection caused different degrees of inflammation in the hepatopancreas, gills, and intestines of M. rosenbergii. The detection of virulence-related genes revealed that the FSNM-1 strain carried colonization factor antigen (cfa1, cfa2), ureases (ureG, ureF, ureD, ureE), and outer membrane protein (ompX), and virulence factor detection showed that the FSNM-1 strain had lecithinase, amylase, lipase, gelatinase, and hemolysin activities but did not produce protease and DNase activities. To investigate the immune response of M. rosenbergii to C. freundii, the expression levels of ALF3, MyD88, SOD, proPO, TRAF6, and TNF immune-related genes were monitored at different points of time in the hepatopancreas, gills, intestines, and hemocytes of M. rosenbergii after infection. The results demonstrated a significant upregulation in the expression levels of the ALF3, MyD88, SOD, proPO, TRAF6, and TNF genes in M. rosenbergii at the early stage of C. freundii infection. This study highlights C. freundii as a major pathogen causing mass mortality in M. rosenbergii and provides valuable insights into its virulence mechanisms and the host’s immune response. Full article

The maritime industry’s increasing integration of IT/OT systems into vessel operations has significantly elevated its exposure to cyber–physical threats, making the development of effective cyber risk management strategies a necessity. This paper provides an outlook of the current landscape of cyber security threats and vulnerabilities for the maritime sector and vessels. An outline of the relevant governmental and industry directives, standards, and guidelines for cyber security in maritime vessels is given. Considering maritime vessels as critical elements of the maritime critical infrastructure sector, a number of relevant cyber–physical security assessment methods are presented. Bridging cyber–physical security, process safety, and security, API SRA (American Petroleum Institute Security Risk Analysis) and BTA (Bow-Tie Analysis) are presented as the most applicable cyber–physical security assessment methods for complex maritime vessels, such as an offshore oil and gas drillship. The scenario of a cyber-attack on the Dynamic Positioning (DP) system of a drillship is presented with the use of API SRA and BTA. The difficulties in the implementation of NIST CSF v2.0 and IACS UR E26 and UR E27 in the maritime sector are also discussed. The need for intensified research on and the formulation of bespoke cyber security measures to mitigate the evolving cyber threats within the maritime domain is highlighted. The need for the allocation of training and resources for the reinforcement of the capacity of a maritime vessel’s crew in the mitigation of cyber threats and safe maritime operations is emphasized. Full article

Straw returning enhances soil fertility and increases corn yield, but the impact on soil fertility varies with different incorporation methods. To explore the optimal straw-returning method, this study, based on a long-term field experiment, investigated the following different corn-straw-returning methods: deep plowing and straw returning (B), rotary tillage and straw returning (RT), crushing and mixing straw returning (TM), pulverized cover straw returning (C), high-stubble-retention straw returning (LHS), strip cover (S), and flat no-tillage without straw returning (CK). High-throughput sequencing technology was employed to analyze the soil bacterial community composition and structural changes under different straw-returning methods. The study further explored the relationships between the soil bacterial community and nutrient content. The results indicated that different straw-returning methods altered the composition and structure of the soil bacterial community. The TM treatment significantly increased the richness and diversity of the soil bacterial communities. Shredding and covering (C and TM) effectively improved the soil nutrient content and bacterial community structure. In the C treatment, the abundance of Blastococcus, Nocardioides, and Microvirga increased the most, by 241.02%, 77.79%, and 355.08%, respectively, compared with CK. In the TM treatment, Pseudarthrobacter showed the highest abundance, increasing by 343.30%. The genes involved in soil carbon hydrolysis (pulA), nitrification (hao), organic nitrogen degradation and synthesis (gudB), and the nitrogen limitation response (glnR) significantly decreased by 56.21%, 78.75%, 66.46%, and 67.40%, respectively, in the C treatment. The genes involved in soil carbon hydrolysis (IMA), carbon fixation (pccB-A), methane metabolism (moxF), nitrate reduction in soil (nirD), organic nitrogen degradation and synthesis (gdh, ureAB, ureE), and phosphate absorption (glpT) significantly increased by 93.37%, 92.68%, 95.00%, 23.42%, 35.40%, 114.21%, 59.14%, and 75.86%, respectively, in the C treatment. The nitrate reduction gene (nrfA) significantly increased by 80.27% in the TM treatment. Therefore, we concluded that straw primarily stimulates the activity of bacterial communities and regulates the bacterial community by changing the relative abundance of the soil microorganisms and functional genes, thereby improving the soil nutrient content. This study considered pulverized cover straw returning and crushing and mixing straw returning to be the most reasonable methods. Full article

When a robot performs tasks such as assembly or human–robot interaction, it is inevitable for it to collide with the unknown environment, resulting in potential safety hazards. In order to improve the compliance of robots to cope with unknown environments and enhance their intelligence in contact force-sensitive tasks, this paper proposes an improved admittance force control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance. In addition, this paper proposes an improved Deep Deterministic Policy Gradient (DDPG)-based optimizer, which is combined with the Gaussian process (GP) model to optimize the admittance parameters. In order to verify the feasibility of the algorithm, simulations and experiments are carried out in MATLAB and on a UR10e robot, respectively. The experimental results show that the algorithm improves the convergence speed by 33% in comparison to the general model-free learning method, and has better control performance and robustness. Finally, the adjustment time required by the algorithm is 44% shorter than that of classical adaptive admittance control. Full article

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut’s Elitex fiber outperformed Shieldex’s fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm. Full article

SINA (Seven in absentia) E3 ubiquitin ligases are a family of RING (really interesting new gene) E3 ubiquitin ligases, and they play a crucial role in regulating plant growth and development, hormone response, and abiotic and biotic stress. However, there is little research on the SINA gene family in U. rhynchophylla. In this study, a total of 10 UrSINA genes were identified from the U. rhynchophylla genome. The results of multiple sequence alignments and chromosomal locations show that 10 UrSINA genes were unevenly located on 22 chromosomes, and each UrSINA protein contained a SINA domain at the N-terminal and RING domains at the C-terminal. Synteny analysis showed that there are no tandem duplication gene pairs and there are four segmental gene pairs in U. rhynchophylla, contributing to the expansion of the gene family. Furthermore, almost all UrSINA genes contained the same gene structure, with three exons and two introns, and there were many cis-acting elements relating to plant hormones, light responses, and biotic and abiotic stress. The results of qRT-PCR show that most UrSINA genes were expressed in stems, with the least expression in roots; meanwhile, most UrSINA genes and key enzyme genes were responsive to ABA and MeJA hormones with overlapping but different expression patterns. Co-expression analysis showed that UrSINA1 might participate in the TIA pathway under ABA treatment, and UrSINA5 and UrSINA6 might participate in the TIA pathway under MeJA treatment. The mining of UrSINA genes in the U. rhynchophylla provided novel information for understanding the SINA gene and its function in plant secondary metabolites, growth, and development. Full article