Search Results (91)

Exoskeleton robots function as augmentation systems that establish mechanical couplings with the human body, substantially enhancing the wearer’s biomechanical capabilities through assistive torques. We introduce a lumbar spine-assisted exoskeleton design based on Variable-Stiffness Pneumatic Artificial Muscles (VSPAM) and develop a dynamic adaptation mechanism bridging the pneumatic drive module with human kinematic intent to facilitate human–robot cooperative control. For kinematic intent resolution, we propose a multimodal fusion architecture integrating the VGG16 convolutional network with Long Short-Term Memory (LSTM) networks. By incorporating self-attention mechanisms, we construct a fine-grained relational inference module that leverages multi-head attention weight matrices to capture global spatio-temporal feature dependencies, overcoming local feature constraints inherent in traditional algorithms. We further employ cross-attention mechanisms to achieve deep fusion of visual and kinematic features, establishing aligned intermodal correspondence to mitigate unimodal perception limitations. Experimental validation demonstrates 96.1% ± 1.2% motion classification accuracy, offering a novel technical solution for rehabilitation robotics and industrial assistance. Full article

Effective bone tissue regeneration remains pivotal in implant dentistry, particularly for edentulous patients with compromised alveolar bone due to atrophy and sinus pneumatization. Biomaterials are essential for promoting regenerative processes by supporting cellular recruitment, vascularization, and osteogenesis. This study presents the development and characterization of a novel lithography-printed ceramic β-TCP scaffold, with a macro/micro-porous lattice, engineered to optimize osteoconduction and mechanical stability. Morphological, structural, and biomechanical assessments confirmed a reproducible microarchitecture with suitable porosity and load-bearing capacity. The scaffold was also employed for maxillary sinus augmentation, with postoperative evaluation using micro computed tomography, synchrotron imaging, histology, and Fourier Transform Infrared Imaging analysis, demonstrating active bone regeneration, scaffold resorption, and formation of mineralized tissue. Advanced imaging supported by deep learning tools revealed a well-organized osteocyte network and high-quality bone, underscoring the scaffold’s biocompatibility and osteoconductive efficacy. These findings support the application of these 3D-printed β-TCP scaffolds in regenerative dental medicine, facilitating tissue regeneration in complex jawbone deficiencies. Full article

Background/Objectives: Radial artery occlusion (RAO) following hemostasis after coronary procedures is the most common complication, with a highly variable incidence (1–33%). While it is well established that the patent hemostasis technique reduces RAO rates, it remains unclear which device should be preferred. The wide variety of available radial hemostasis devices makes it necessary to identify those associated with a lower incidence of complications. Methods: Literature from 2016 to 2021 was reviewed through a systematic search in PubMed, CINAHL, Cochrane, and Embase databases. Only randomized controlled trials (RCTs) involving adult patients undergoing percutaneous transradial coronary procedures were included. Devices considered included pneumatic compression devices, manual compression, elastic bandages, and hemostatic dressings. The review process followed PRISMA guidelines. Two random-effects frequentist network meta-analyses were conducted to compare the effects of 16 and 9 radial hemostasis devices on RAO incidence at 24 h and 30 days after the procedure. Results: A total of 17 RCTs were included. The network meta-analysis (NMA) showed a protective effect at the 24 h endpoint for both double-balloon devices and pneumatic compression devices adjusted to mean arterial pressure. At the 30-day endpoint, significant differences were observed among pneumatic compression, chitosan-based PADs, mechanical compression devices, and adjustable elastic bandages. Conclusions: Although some treatments with specific devices significantly differ from the reference treatment, the limited availability of data to assess RAO at 30 days and a certain heterogeneity between devices indicate the need for further investigation. Full article

The water hammer phenomenon represents a significant challenge to the safe and efficient operation of pressurised water systems. This study investigates the application of hydro-pneumatic tanks (HPTs) as protective devices against transient flow events, with a particular focus on their integration into simplified modelling frameworks. Rigid and elastic water column models are examined, and their performance is evaluated through a representative case study. A multi-criteria decision matrix was employed to select a suitable simulation tool, leading to the adoption of the ALLIEVI software for implementing both modelling approaches. Comparative results indicate that the rigid water column model offers a favourable compromise between accuracy and computational efficiency under appropriate conditions. This supports its practical application in installing HPTs in design and operational scenarios. To further assess the predictive capacity of each model, a confusion matrix analysis was conducted across 57 scenarios. This approach proved effective in evaluating the models’ ability to anticipate pipeline rupture based on the initial configuration of the hydraulic installation. The elastic model achieved accuracy levels ranging from 90.7% to 100%, whereas the rigid water column model exhibited a slightly broader accuracy range, from 76.7% to 97.7%. These findings suggest that integrating machine learning techniques could enhance the rapid assessment of failure risks in water utility networks. Such tools may enable operators to determine in advance whether a given operating condition will likely lead to system failure, thus improving resilience and decision-making in managing pressurised pipeline systems. Full article

This study presents a comparative analysis of various machine learning algorithms to evaluate their performance in diagnosing faults within automated manufacturing systems. The primary objective is to identify the most effective model for classifying equipment failures based on historical data. Several algorithms were selected, including support vector machines (SVM), Decision trees, boosting, random forest, k-nearest neighbors (KNN), stacking, and artificial neural networks. The research began with the collection of a dataset using an Arduino-based system with sensors (temperature, electrical current, differential pressure, vibration, and sound) to monitor the equipment’s operational condition. Faults were intentionally induced in a motor, an electrovalve, and a pneumatic cylinder. The data were then processed in a Python environment, undergoing normalization and dimensionality reduction. The models were evaluated through cross-validation and compared using metrics such as precision, recall, F1-score, and accuracy. Results indicated that all models performed well, with the SVM algorithm showing the best overall performance, with an average fault diagnosis accuracy of 91.62% when trained on the full dataset and 66.83% under extreme class imbalance. In contrast, decision trees demonstrated lower generalization ability. This study provides insights for future fault diagnosis research using machine learning and offers recommendations for implementing such technologies in industrial environments. Full article

This study investigates the application of proportional–integral–derivative (PID) control enhanced with an optimized particle swarm optimization (OPSO)-type neural network (NN) to improve the control performance of a pneumatic servo system. Traditional PID methods struggle with inherent nonlinearities in pneumatic servo systems. To address this limitation, we integrate two OPSO-type NNs within the PID framework, thereby developing a robust control strategy that compensates for these nonlinear characteristics. The OPSO-type NNs are particularly efficient in solving complex optimization problems without requiring differential information, demonstrating superior simplicity and efficacy compared to traditional methods, such as genetic algorithms. In our approach, one of the OPSO-type NNs is utilized to tune the PID controller gains, while the other adjusts the control output. The experimental results show that the proposed method enhances the position control accuracy of the pneumatic servo system. Furthermore, this approach holds promise for improving the responsiveness, stability, and disturbance suppression capabilities of pneumatic systems, paving the way for advanced control applications in this field. Full article

This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device. Full article

Pneumatic force control has a broad application background in the automation field, such as in industrial polishing, robotic grasping, and humanoid robots. Nonlinear hysteresis characteristics are one of the major factors that affect the feedforward force control performance of a pneumatic system. The primary motivation of this paper is to develop an accurate feedforward actuating force control method for a single-acting pneumatic cylinder with a nonlinear hysteresis characteristic. A data-driven neural network modeling method is presented to achieve accurate actuating force modeling. The modeling accuracy of the neural network model under different configurations of the input layer is quantitatively analyzed to determine the essential modeling variables. The real-time execution speed of neural network models with different numbers of hidden neurons is evaluated to achieve a balance between the modeling accuracy and the real-time computing speed of the neural network model. Then, a single-acting pneumatic system is fabricated to experimentally verify the effectiveness of the proposed modeling and control method. The experimental results reveal that the actuating force can achieve ideal tracking of the target. In both the loading and the unloading process, the amplitude of the control error is less than 0.5 N. The overall RMS value of the control error is about 1 N. An instruction smoothing operation could reduce the percentage overshoot and steady-state error of the feedforward step actuating force control. Full article

McKibben’s muscle (MKM) is the most adopted among the different types of pneumatic artificial muscles (PAMs) due to its mechanical performance and versatility. Several geometric parameters, including the diameter, thickness, and length of the inner elastic element, as well as functional conditions, such as shortening ratio and feeding pressure, influence the behaviour of this actuator. Over the years, analytical and numerical models have been defined to predict its deformation and developed forces. However, these models are often identified under simplifications and have limitations when integrating new parameters that were not initially considered. This work proposes a hybrid approach between finite element analyses (FEAs) and machine learning (ML) algorithms to overcome these issues. An MKM was numerically simulated as the chosen parameters changed, realizing the MKM dataset. The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. The results demonstrate the effectiveness of ML techniques in predicting the behavior of MKMs while offering flexibility for integrating additional parameters. Therefore, this paper highlights the potential of ML approaches in the mechanical design of MKM according to the field of use and application. Full article

Soft robots with compliant bodies offer safe human–robot interaction as well as adaptability to unstructured dynamic environments. However, the nonlinear dynamics of a soft robot with infinite motion freedom pose various challenges to operation and control engineering. This research explores the motion of a pneumatic soft robot under diverse loading conditions by conducting finite element analysis (FEA) and using machine learning. The pneumatic soft robot consists of two parallel hyper-elastic tubular chambers that convert pneumatic pressure inputs into soft robot motion to mimic an elephant trunk and its motion. The body of each pneumatic chamber consists of a series of bellows to effectively facilitate the expansion, contraction, and bending of the body. The first chamber spans the entire length of the soft robot’s body, and the second chamber spans half of it. This unique asymmetric design enables the soft robot to bend and curl in various ways. Machine learning is used to establish a forward kinematic relationship between the pressure inputs and the motion responses of the soft robot using data from FEA. Accordingly, this research employs an artificial neural network that is trained on FEA data to estimate the reachable workspace of the soft robot for given pressure inputs. The trained neural network demonstrates promising estimation accuracy with an R-squared value of 0.99 and a root mean square error of 0.783. The workspaces of asymmetric double-chamber and single-chamber soft robots were compared, revealing that the double-chamber robot offers approximately 185 times more reachable workspace than the single-chamber soft robot. Full article

This paper presents a novel soft crawling robot controlled by gesture recognition, aimed at enhancing the operability and adaptability of soft robots through natural human–computer interactions. The Leap Motion sensor is employed to capture hand gesture data, and Unreal Engine is used for gesture recognition. Using the UE4Duino, gesture semantics are transmitted to an Arduino control system, enabling direct control over the robot’s movements. For accurate and real-time gesture recognition, we propose a threshold-based method for static gestures and a backpropagation (BP) neural network model for dynamic gestures. In terms of design, the robot utilizes cost-effective thermoplastic polyurethane (TPU) film as the primary pneumatic actuator material. Through a positive and negative pressure switching circuit, the robot’s actuators achieve controllable extension and contraction, allowing for basic movements such as linear motion and directional changes. Experimental results demonstrate that the robot can successfully perform diverse motions under gesture control, highlighting the potential of gesture-based interaction in soft robotics. Full article

This paper proposes a multifunctional soft robotic gripper for a Dobot robot to handle sensitive products. The gripper is based on pneumatic network (PneuNet) bending actuators. In this study, two different models of PneuNet actuators have been studied, designed, simulated, experimentally tested, and validated using two different techniques (3D printing and molding) and three different materials: FilaFlex 60A (3D-printed), Elastosil M4601, and Dragonskin Fast 10 silicones (with molds). A new soft gripper design for the Dobot robot is presented, and a new design/production approach with molds is proposed to obtain the gripper’s PneuNet multifunctional actuators. It also describes a new control approach that is used to control the PneuNet actuators and gripper function, using compressed air generated by a small compressor/air pump, a pressure sensor, a mini valve, etc., and executing on a low-cost controller board—Arduino UNO. This paper presents the main simulation and experimental results of this research study. Full article

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability. Full article

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. Full article

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals. Full article