Force Information Research Articles

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

Relationship Between Resultant Force Vector Acting on Human Organs From Food Bolus and the Bolus Configuration During Swallowing Using Numerical Swallowing Simulation With Moving Particle SimulationMethod.

This study investigates the forces exerted on organs during swallowing, specifically focusing on identifying forces other than those resulting from direct organ contact. Using a swallowing simulator based on the moving particle method, we simulated the swallowing process of healthy individuals upon the ingestion of thickened foods, which were simulated as shear-thinning flow without yield stress. We extracted the resultant force vectors acting on the organs and shape of the bolus at each time interval. The simulation results confirmed that the bolus originates from tongue movement and is transferred between the oral cavity and pharynx, with each organ's coordinated movements with the tongue occurring at their respective positions, as indicated by the balance of the resultant force vectors. Utilizing the information about the resultant force vectors obtained through simulations, we calculated the physical parameters of impulse, energy, and power. The variations in these physical parameters were aligned with the behaviors of both the biological system and the food bolus during swallowing. The force values calculated from the simulations closely approximate the theoretical values. Furthermore, the forces calculated from the simulations were relatively smaller than the force values derived from pressure information, such as that from high-resolution manometry and tongue pressure sensors. This difference can be attributed to the simulations extracting only the forces exerted on the organ by the food bolus. Force information on organs has the potential to provide a new interpretation of conventional mechanical indicators such as manometry and tongue pressure sensors.

Force Measurement Technology of Vision‐Based Tactile Sensor

Marker‐type vision‐based tactile sensors (VTS) realize force sensing by calibrating marker vector information. The tactile visualization can provide high‐precision and multimodal force information to promote robotic dexterous manipulation development. Considering VTS's contribution to force measurement, this article reviews the advanced force measurement technologies of VTSs. First, the working principle of marker‐type VTSs is introduced, including single‐layer markers, double‐layer markers, color coding, and optical flow. Then, the relationship between the marker type and the category of force measurement is discussed in detail. On this basis, the process of marker feature extraction is summarized, including image processing and marker‐matching technologies. According to the learning approach, force measurement methods are classified into physical and deep learning models. Further, branches of each method are analyzed in terms of input types. Combined with measuring range and precision, the correlation of sensor design, materials, and recognition methods to force measurement performance is further discussed. Finally, the difficulties and challenges are analyzed, and future developments are proposed. This review aims to deepen understanding of the research progress and applications and provide a reference for the research community to promote technology generations in related fields.

Pengaruh Tinggi Bukaan Katup terhadap Torsi dan Daya pada Motor Besin 4 Langkah Berkapasitas 200CC

Automobiles are now the primary mode of transportation due to rapid technological advancements. One method for accomplishing ideal volumetric effectiveness and augment power is to change the camshaft. This study means to grow understanding in the auto world in regards to power and force and to assess the impact of changes in camshaft lift level on a 200cc limit bike utilizing pertalite fuel. The information from the review is introduced in tables and examined utilizing the two-way ANOVA technique and charts. The results of the analysis indicate that a comparison of the power data obtained by adding the lift height (test 3) yields a result of 16 66 HP at 8500 RPM. In the mean time, the force information demonstrates the way that the force examination of the three tests can be supposed to be the most elevated information of the three tests, to be specific (test 3) which is completed by adding a high lift to get a force of 17 59 Nm at engine rotation 5000 RPM. Utilized DeepL.com (free version) for translation.

Quantifying the aerodynamic admittance and spanwise coherence functions of buffeting lift for bridge decks through the measurement of segmental forces

The assessment of buffeting response of long span bridges relies significantly on the aerodynamic admittance and spanwise coherence functions of buffeting forces acting on bridge decks. This study presents a novel approach to derive admittance and coherence functions of buffeting lift on bridge decks, utilizing wind tunnel measurement of forces on spanwise distributed segments. The approach is developed by establishing connections of the power spectrum and coherence function of segmental lift with the admittance and coherence functions of strip lift. This study also explores the direct estimation of the generalized buffeting forces on long span bridges from the segmental force, eliminating the need of extracting admittance and coherence functions of strip force. The methodology is firstly validated for a thin plate with theoretical force information, follow by its application to a twin-box bridge deck using wind tunnel data. The investigation assesses the influence of a pre-assumed coherence model on the identified admittance and coherence functions and its consequential impact on the generalized buffeting forces of long span bridges. The proposed approach offers a practical and efficient means to quantify buffeting forces acting on bridge decks with intricate configurations, such as truss sections, where surface pressure measurement may pose challenges.

Liquid thermophysical properties of Ag-Si alloy based on deep learning potential

The knowledge of the thermophysical properties of liquid metals and alloys is essential for expanding the materials database and designing materials with good properties. In this work, we developed an interatomic potential using a deep neural network (DNN) algorithm for liquid Ag-Si alloys. Compared with ab initio molecular dynamics (AIMD) results, the DNN potential provided a good description of the information of energy, force, and structure features of the system in the simulated temperature range. Through this potential, we can obtain the thermophysical properties of different compositions of liquid alloys by simulation way. The computed thermophysical properties are in excellent agreement with the reported experimental data. The analysis of local structure indicates that the liquid ordering and stability strengthen upon cooling at the atomic level, eventually leading to an increase in thermophysical properties.

Design optimization of two-dimensional wireless passive force sensor based on finite element analysis

Abstract A two-dimensional wireless and passive sensor based on finite element analysis (FEA) is designed, analyzed, optimized, and tested. The sensor is using the inverse magnetostrictive effect. The sensor included three pieces of sensitive elements that stick to the sensitive area of the elastomer. The FEA mothed is used to assist the design, analysis, and optimization process. The strain-sensitive area of the tension and torque are located in different areas, according to the finite element analysis. When the sensor was fabricated, the sensor’s output performance was tested. The experiment results show that the effective range of tension is 40 N, and the effective range of torque is 4 N·m. The optimized smart structure has a good decoupling effect, which can reduce coupling between dimensions. Finally, the two-dimensional force information can be detected passively and wirelessly.

An output-only damage identification method based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction

The absence of excitation measurements may pose a huge challenge in the application of many damage identification methods since it is difficult to acquire the external excitations, such as wind load, traffic load. To deal with this issue, a novel output-only structural damage identification approach based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction with Bayesian inference regularization is developed. On the one hand, heterogeneous measurements (e.g., displacements, strains, accelerations) are rescaled and reconstructed with the aid of Bayesian inference regularization technique. Structural damages are identified by minimizing the discrepancies between the measured and reconstructed responses. On the other hand, to solve the optimization-based inverse problem, a reinforcement-aided evolutionary algorithm, named Q-learning hybrid evolutionary algorithm (QHEA), integrating Jaya algorithm, differential algorithm, and Q-learning algorithm is proposed as search tool. To validate the feasibility and applicability of the proposed method, numerical studies on a three-span beam structure and laboratory tests on a five-story steel frame structure are carried out. The effects of data rescaling and data fusion on response reconstruction and damage identification are also investigated. The results clearly demonstrate the superiority of QHEA over other heuristic algorithms and heterogeneous data fusion over a single type of measurement. It is shown that both the locations and extents of the damaged elements can be accurately identified by the proposed method without the information of input force.

Hybrid compliant control with variable-stiffness wrist for assembly and grinding application

This research presents a novel robot system that combines active and passive components to enhance compliance and dependability. The system is based on a continuous variable stiffness wrist. A wrist was created that met the requirements and a combination of active and passive control methods was suggested to insert and regulate forces effectively. The control strategy is based on the Cosserat rod model, with the fundamental concept being calculating the position and orientation of the component using data on the force exerted during contact between the parts and the stiffness of the contact between the shaft and hole components. This process converts the hard assembly into a flexible contact. Compliance is monitored via force and vision sensors, which allows for the shaft-hole assembly operation to be carried out even with attitude alignment problems, resulting in a notable decrease in the precision needed for component alignment. Initially, the camera supplies the first positional data of the shaft component for the robotic system. In addition, the performance of the wrist with variable stiffness is evaluated in terms of stiffness. Additionally, the calculation of relative deformation between components is examined using contact force information. Moreover, a robust active/passive hybrid insertion control technique, which relies on contact force, is proposed. Finally, the shaft-hole assembly task substantiates the necessity for contact force monitoring in the insertion assembly process. This control technique has demonstrated its efficacy in ensuring passive-compliant assembly performance. Furthermore, the variable stiffness wrist has been employed in robotic grinding for surfaces with curved contours to validate its effectiveness.

Local impedance drop-guided versus lesion size index-guided pulmonary vein isolation.

Local tissue impedance drop (LID) and lesion size index (LSI) technologies are valuable for predicting effective lesion formation. This study compares the acute and long-term efficacy of LID-guided versus LSI-guided pulmonary vein isolation (PVI) for atrial fibrillation treatment. We retrospectively analyzed two patient groups undergoing radiofrequency PVI. In the LID-guided group (n = 35), ablation was performed without contact force monitoring, stopping at the LID plateau (target LID 12 Ohm posterior, 16 Ohm anterior). In the LSI-guided group (n = 31), ablation used contact force information with target LSI (5 anterior, 4 posterior). Both groups utilized a power of 40 W anterior and 30 W posterior, with < 6mm inter-lesion distance. Gap mapping and touch-up ablation were done if necessary. PVI was achieved with a significantly shorter ablation time in the LSI-guided group (25min [21;31] vs 30 [27;35], p = 0.035). PV gaps were more frequent in the LID-guided group (74% vs 42%, p = 0.016). Over 11.5 ± 2.9months follow-up, arrhythmia recurrence was higher in the LID-guided group (34.3% vs 16.1%, p = 0.037). A redo procedure performed in 10 (28.6%) patients in the LID-guided group and 3 (9.7%) in the LSI-guided group showed chronic PV reconnections in 7 out of 10 (70%) and 2 out of 3 (67%) patients, respectively. LSI-guided ablation results in shorter ablation time and fewer PV gaps compared to LID-guided ablation. Despite initial success, LID-guided ablation had higher arrhythmia recurrence and PV reconnections during long-term follow-up compared to LSI-guided ablation.

Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

Constant force grinding controller for robots based on SAC optimal parameter finding algorithm

Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

Attack detection method for encrypted wave‐variable‐based bilateral control systems

AbstractThis study presents an energy‐based attack detection method involving an encrypted bilateral control system using wave variables. In the considered bilateral control system, the leader and follower receive the follower's force information and the leader's velocity information, respectively, through the wave variables. The considered attack model multiplies the wave variables by an attack parameter, which is possible due to the malleability of the encryption scheme. The bilateral control system will be destabilized if the attacker chooses a relatively large parameter value. This motivates in developing a passivity observer for each leader and follower side to compute the total energy and constructing an energy‐based detection method that can be incorporated into the encrypted bilateral control system and is summarized in the presented theorem. Furthermore, this study provides a specific design for reasonable threshold parameters concerning the control system energy. The theorem and the experimental validation confirm that the developed encrypted wave‐variable‐based bilateral control system with the proposed attack detector is secure and effective as a countermeasure against malleability‐based attacks.

Plantar Haptic Display to Estimate the Position of the Projected Center of Gravity of a Humanoid

Humans can perform stable motion with a sense of stability that involves identifying their center of gravity point with plantar sensory system. By contrast, the stability of a humanoid robot is inferior to that of a human. If the sense of stability of a humanoid robot is presented to a human who operates the robot in real time, the operator can control the robot more stably. However, studies wherein humans recognize the projected center of gravity point with a plantar haptic display are lacking. In this study, we investigated the possibility of estimating the projected center of gravity point of a small humanoid robot from only the foot force information. We propose a plantar haptic display to reproduce the foot reaction force on four points of a single sole as large as the force during human running. We conducted an experiment to verify the estimated center of gravity point using the foot reaction forces of a small robot with and without scaling by a weight ratio between an operator and the robot. The results indicated that it is difficult to estimate the projected center of gravity point without scaling; however, it is possible to estimate the movement of the projected center of gravity point with a maximum error of [Formula: see text][Formula: see text]mm with scaling.

Evaluating vehicle trafficability on soft ground using wheel force information

In the areas of aerospace and military industry, wheeled vehicles are expected to have the ability of passing various ground surfaces, including lunar soil, sand, marsh, mud flat, etc. This makes vehicle trafficability on soft ground become a very hot research topic. There are very a few difficulties in the present research of vehicle trafficability on soft ground, such as obtaining wheel-ground interaction information, inaccurate identification of soil mechanical characteristics parameters, and single evaluation index. In this paper, a novel approach of evaluating the vehicle trafficability on soft ground using wheel force information is proposed. As parts of the proposed approach, the methods of obtaining wheel force information, identification of soil mechanical characteristics parameters and integated method of trafficability evaluation, are discussed in detail. The proposed approach was validated through a practical test.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Development and calibration of large deformation-compliant six-axis force sensor

Improving the measurement accuracy and minimising the coupling between directions are the keys to researching the compliant six-axis force sensors. The use of a six-axis force sensor to accurately monitor the ground reaction force and centre of pressure during human motion is of great significance in the fields of biomechanics and pathological gait diagnosis. Although complete force information can be obtained using a commercial six-axis force sensor, its high stiffness affects the natural gait and easily leads to human fatigue. A compliant six-axis force sensor based on a flexible optical waveguide is proposed, in which the force and torque of six dimensions are detected by reasonably arranging six modular sensing units, and the mechanical decoupling of some dimensions is realised in theory. For the interdimensional coupling and error caused by machining process factors, as well as the nonlinear relationship between the input and output of the proposed compliant six-axis force sensor, a DE-RBF decoupling algorithm is proposed to decouple the calibration data. Compared with the least squares method (LSM) and the radial basis function (RBF) neural network decoupling algorithm, the obtained type-I errors were reduced by 87.7629%, 43.6265%, respectively, and type-II errors by 35.3312%, 56.9162%, respectively. The decoupling result’s maximum type-I and type-II errors were reduced from 7.7125% and 2.7382% in LSM and 3.1029% and 2.8917% in RBF to 0.5916% and 0.9558%, respectively. The measurement accuracy of the compliant six-axis force sensor was significantly higher; however, the time effectiveness of the proposed DE-RBF decoupling algorithm was slightly lower than that of the RBF neural network by 2.47%. In conclusion, the decoupling accuracy and timeliness of the proposed DE-RBF decoupling algorithm can satisfy the requirements of compliant six-axis force sensors to monitor low-frequency biomechanical signals, such as human motion.

Soft Monolithic Shielded Sensors to Measure Shear and Normal Forces for Local Slip Detection

AbstractSoft force sensors enable closed loop control or autonomous operation of compliant robot systems. A stretchable force sensing surface containing an array of independent sensor units is reported. This design allows for the measurement of local shear and normal forces at multiple locations, independently, providing real‐time, multi‐axis force information at each unit. The sensor array is based on capacitive strain sensing, using liquid metal electrodes and air microchannel for high sensitivity. Electromechanical interference between adjacent sensor units is addressed and eliminated through design optimization and the use of compliant electrical shield layers. A 1D sensor array is applied to gripping tasks, where the independent sensor readings enable detecting local and partial slipping. This study illustrates how the device can be used to explore the dynamics of local forces during the stages of pick up, slip, and release.

Balance Evaluation Based on Walking Experiments with Exoskeleton Interference.

The impairment of walking balance function seriously affects human health and will lead to a significantly increased risk of falling. It is important to assess and improve the walking balance of humans. However, existing evaluation methods for human walking balance are relatively subjective, and the selected metrics lack effectiveness and comprehensiveness. We present a method to construct a comprehensive evaluation index of human walking balance. We used it to generate personal and general indexes. We first pre-selected some preliminary metrics of walking balance based on theoretical analysis. Seven healthy subjects walked with exoskeleton interference on a treadmill at 1.25 m/s while their ground reaction force information and kinematic data were recorded. One subject with Charcot-Marie-Tooth walked at multiple speeds without the exoskeleton while the same data were collected. Then, we picked a number of effective evaluation metrics based on statistical analysis. We finally constructed the Walking Balance Index (WBI) by combining multiple metrics using principal component analysis. The WBI can distinguish walking balance among different subjects and gait conditions, which verifies the effectiveness of our method in evaluating human walking balance. This method can be used to evaluate and further improve the walking balance of humans in subsequent simulations and experiments.

Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures

In this research, we utilized density functional theory (DFT) computations to perform ab initio molecular dynamics simulations and static calculations on graphene, hexagonal boron nitride, and their heterostructures, subjecting them to strains, perturbations, twist angles, and defects. The gathered energy, force, and virial information informed the creation of a training set comprising 1253 structures. Employing the Neural Evolutionary Potential framework integrated into Graphics Processing Units Molecular Dynamics, we fitted a machine learning potential (MLP) that closely mirrored the DFT potential energy surface. Rigorous validation of lattice constants and phonon dispersion relations confirmed the precision and dependability of the MLP, establishing a solid foundation for subsequent thermal transport investigations. A further analysis of the impact of twist angles uncovered a significant reduction in thermal conductivity, particularly notable in heterostructures with a decline exceeding 35%. The reduction in thermal conductivity primarily stems from the twist angle-induced softening of phonon modes and the accompanying increase in phonon scattering rates, which intensifies anharmonic interactions among phonons. Our study underscores the efficacy of the MLP in delineating the thermal transport attributes of two-dimensional materials and their heterostructures, while also elucidating the micro-mechanisms behind the influence of the twist angle on thermal conductivity, offering fresh perspectives for the design of advanced thermal management materials.