Force Estimation Method Research Articles

Video2Haptics: Converting Video Motion to Dynamic Haptic Feedback with Bio-Inspired Event Processing.

In cinematic VR applications, haptic feedback can significantly enhance the sense of reality and immersion for users. The increasing availability of emerging haptic devices opens up possibilities for future cinematic VR applications that allow users to receive haptic feedback while they are watching videos. However, automatically rendering haptic cues from real-time video content, particularly from video motion, is a technically challenging task. In this article, we propose a novel framework called "Video2Haptics" that leverages the emerging bio-inspired event camera to capture event signals as a lightweight representation of video motion. We then propose efficient event-based visual processing methods to estimate force or intensity from video motion in the event domain, rather than the pixel domain. To demonstrate the application of Video2Haptics, we convert the estimated force or intensity to dynamic vibrotactile feedback on emerging haptic gloves, synchronized with the corresponding video motion. As a result, Video2Haptics allows users not only to view the video but also to perceive the video motion concurrently. Our experimental results show that the proposed event-based processing methods for force and intensity estimation are one to two orders of magnitude faster than conventional methods. Our user study results confirm that the proposed Video2Haptics framework can considerably enhance the users' video experience.

A Novel Estimating Algorithm of Critical Driving Parameters for Dual-Motor Electric Drive Tracked Vehicles Based on a Nonlinear Observer and an Adaptive Kalman Filter

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms.

Design and Modelling of Continuum Robot for Endoscopic Submucosal Dissection Surgery With Lifting Force Estimation.

Endoscopic submucosal dissection (ESD) is an effective treatment for early-stage gastrointestinal cancers. However, traditional surgical instruments lack accuracy and force-sensing. A new type of continuum robot for ESD is designed. An accurate static model of the proposed continuum robot is established, considering cases where the robot bends into C-shapes and S-shapes. A force estimation method based on an accurate static model is proposed. Then, the accuracy of the static model and force estimation is verified through experiments. Finally, an ex-organ experiment is carried out. The average position error of the proposed static model is 0.72mm, accounting for 2.57% of the total robot length. The average error of force estimation is 19.53mN. By gripping and cutting ex-porcine gastric mucosa, the robot's functionality is validated. This paper contributes to precise control and safe interaction of continuum robots.

Structural Design and Horizontal Wave Force Estimation of a Wall-Climbing Robot for the Underwater Cleaning of Jackets

Currently, divers face significant safety risks when cleaning marine organisms from the steel structures of offshore underwater platform jackets. Consequently, utilizing robots instead of divers to carry out underwater biofouling removal operations will be an important development direction for the underwater maintenance of offshore platforms in the future. In this study, a wall-climbing robot was designed to clean marine organisms from the underwater surface of a platform jacket leg. The overall structure of the underwater cleaning wall-climbing robot is introduced, including the cleaning actuator and the variable curvature-adapted connecting rod mechanism. The corresponding relationship between the variable curvature-adapted connecting rod mechanism and the jacket leg is analyzed in detail. The variable curvature-adapted connecting rod mechanism was optimized using a genetic algorithm to ensure that the underwater cleaning wall-climbing robot can adapt to a minimum diameter of 1 m for the jacket leg. By drawing on Airy wave theory and random wave theory, the Airy wave parameters for waves were analyzed under different sea conditions, considering practical application scenarios. By using Fluent software 2022, a 2D numerical wave tank was constructed to simulate waves under various sea conditions, and the wave surface shapes for different sea states were determined. By building on the Morison equation, a method for calculating the horizontal wave forces on the underwater cleaning wall-climbing robot using the equivalent area and equivalent volume is proposed. By using the two aforementioned methods, the horizontal wave forces on the underwater cleaning wall-climbing robot under specific sea states were determined. The horizontal wave forces of the underwater cleaning wall-climbing robot under different sea conditions were analyzed and simulated in a 3D numerical wave tank. By comparing the theoretical analysis results with the numerical simulation results, where the maximum difference at the extreme points is approximately 11%, the feasibility of the proposed horizontal wave force estimation method was verified.

Estimating muscle force based on a neuromuscular decoding approach adaptive to fatigue conditions

Electromyography (EMG) has been extensively employed for precise muscle force estimation. Although methods based on motor unit (MU) activities (the smallest functional unit of the neuromuscular control system) have achieved superior performance, their effectiveness in fatiguing conditions remains unknown. This study aims to investigate the performance of existing MU-based muscle force estimation methods in fatiguing conditions, including the representative neuromuscular decoding (NMD) method and firing rate (FR) −based method. Compared to FR-based method, the NMD method specifically accounts for the differences in force contributions among MUs and the force twitch response of muscle fibers. We conducted fatigue induction experiment to collect force and high-density surface EMG data from the abductor pollicis brevis muscles of eight subjects during their performance of thumb abduction. Subsequently, the analysis of myoelectric manifestation of fatigue was performed and four training–testing strategies were designed to examine the effect of fatigue on force estimation methods. Experimental results indicated that the NMD method achieved superior and stable performance (assessed through root mean square difference between estimated and actual force) across four strategies (p >0.05 revealed by ANOVA), with only slight performance decline when fatigue data was involved in training or/and testing phase. Conversely, fatigue caused significant performance degradation when using the FR-based and other five comparative methods (p <0.05). These findings demonstrate that the NMD approach is adaptive to fatiguing MU activities, which can provide precise and robust muscle force estimation against fatigue in motor control and human–machine interfaces.

Rack force fault tolerance estimation of steer-by-wire system under different resolver faults based on sensor flows

In the context of vehicle intelligence, steer-by-wire (SbW) has great potential for development and has become a current research hotspot. The key challenge for SbW systems is to obtain the nonlinear rack force generated by tire-road interaction, which is the source of the driver's real steering feel and the main disturbance of steering angle tracking control. Existing rack force estimators either are only suitable for non-aggressive steering or assume that the measured signals are reliable. This paper proposes a high-precision rack force estimation algorithm that is robust to primary sensor faults of a SbW system. Further, a rack force estimator based on the established high-order SbW mechanism model is designed, and a detailed stability proof and feedback gain selection guidance are given. The resolver sensor model is provided and analyzed, and a fault detection method based on discrete Fourier transform (DFT) and data fusion is developed, which has good real-time performance and high accuracy. The performance of rack force estimator was verified and compared by simulations and hardware-in-loop (HIL) experiments with various steering maneuvers and different resolver faults. The experiment results show that the proposed rack force estimation method can not only effectively improve the estimation accuracy even in aggressive steering condition and uneven road profiles, but also achieve accurate estimation of the rack force in the case of resolver faults. The proposed method obtains high-precision and high-reliability rack force estimation results while being easily embedded applied in engineering practice.

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Comparative Evaluation of Electro-Mechanical Brake Clamping Force Estimation and Sensor Compensation Control Method for High-Speed-Train

Comparative Evaluation of Electro-Mechanical Brake Clamping Force Estimation and Sensor Compensation Control Method for High-Speed-Train

Cable force estimation method based on UAVs and EMD

Cable force estimation method based on UAVs and EMD

An IMU-Based Ground Reaction Force Estimation Method and Its Application in Walking Balance Assessment.

Walking is one of the most common daily movements of the human body. Therefore, quantitative evaluation of human walking has been commonly used to assist doctors in grasping the disease degree and rehabilitation process of patients in the clinic. Compared with the kinematic characteristics, the ground reaction force (GRF) during walking can directly reflect the dynamic characteristics of human walking. It can further help doctors understand the degree of muscle recovery and joint coordination of patients. This paper proposes a GRF estimation method based on the elastic elements and Newton-Euler equation hybrid driving GRF estimation method. Compared with the existing research, the innovations are as follows. 1) The hardware system consists of only two inertial measurement units (IMUs) placed on shanks. The acquisition of the overall motion characteristics of human walking is realized through the simplified four-link walking model and the thigh prediction method. 2) The method was validated not only on 10 healthy subjects but also on 11 Parkinson's patients and 10 stroke patients with normalized mean absolute errors (NMAEs) of 5.95%±1.32%, 6.09%±2.00%, 5.87%±1.59%. 3) This paper proposes a dynamic balance assessment method based on the acquired motion data and the estimated GRF. It evaluates the overall balance ability and fall risk at four key time points for all subjects recruited. Because of the low-cost system, ease of use, low motion interference and environmental constraints, and high estimation accuracy, the proposed GRF estimation method and walking balance automatic assessment have broad clinical value.

On the robustness of force estimation methods for robot manipulators: An experimental study

Force control of robot manipulators has been extensively studied for the last decades. Whether a direct or an indirect approach is pursued, it is always desirable to know the contact forces/torques, which are usually attained through force sensors. However, there are a variety of reasons for which it may not be desirable to employ them. For instance, they are expensive and not free of noise, and for some applications, they might just be too big to be a practical solution. Following these reasons, there have been many efforts to develop estimation techniques. Some of the main challenges are the need of exact robot models as well as position, velocity, and even acceleration measurements. In this work, a compilation of some of the most employed approaches developed in the last two decades is presented, along with the design of a novel force estimation algorithm. The proposed method is a modification of the well-known Generalized Momentum approach, with the advantages of finite-time convergence and improved performance. A comparative analysis is carried out to elucidate which of the methods delivers a better performance based on two key assumptions presented at the experimental implementation: 1) the robot model is accurate, and 2) the estimated forces/torques should be employed in a control scheme without a previous closed-loop stability analysis. Both assumptions are meant to test the robustness of the different approaches since the former is not realistic, while the latter is not advisable because no separation principle is valid for nonlinear systems. However, from a practical point of view, it is interesting to substitute force measurements with no further analysis. The experimental outcomes show closed-loop stability in all cases.

Thrust force requirements in fault zones with squeezing ground

Shield jamming constitutes a critical hazard in mechanized tunnelling under squeezing conditions. These are often encountered within narrow fault zones interspersed between competent rock layers, where the competent rock exhibits much smaller deformations, thus mobilising shear stresses at the interfaces. This so-called “wall-effect” results in substantially reduced deformations and rock pressures in the fault zone and is thus favourable with respect to shield jamming. Building on existing design nomograms for evaluating the required thrust force in homogeneous ground, this paper proposes two novel, simple and practical thrust force estimation methods that additionally consider the favourable influence of the wall-effect in narrow fault zones. The first method performs an a priori modification of the nomogram input via an equivalent improved ground stiffness that depends on the fault zone length. The second method performs an a posteriori correction of the nomogram output via an analytically determined coefficient, established via multiparametric regression that considers the results of 3450 numerical analyses. Both methods allow for a fast evaluation of the required thrust force in fault zones with reasonable accuracy, thereby providing a valuable tool for design.

Indirect force estimation method under spectrally varying and amplitude-sensitive conditions by estimating the effective fluid path parameter

Indirect methods to estimate the force transmitted from the source to the receiver have been investigated using various approaches to increase accuracy and overcome discrepancies, especially when the measurements are conducted in situ. To implement the indirect force estimation techniques in situ, developing an accurate measurement method and establishing reasonable system models always matter when the force estimations involve nonlinear internal forces such as the upper chamber pressures of the hydraulic mount system. Based on prior research conducted on force estimation in a nonlinear hydraulic mount, a simplified reverse-path spectral method is proposed in this study for estimating the force transmitted from the driving point to the ground by focusing on one effective fluid path parameter. To achieve the purpose of this study, two interfacial force datasets from the non-resonant test were first captured along with spectrally varying and amplitude-sensitive conditions. Second, two different path forces were determined from the rubber and hydraulic paths, and the effective fluid path parameter (EFP) was estimated from the hydraulic path force. The employment of the EFP contributes to estimating the effective internal properties, which show better correlations with the measurements than the estimated results from prior studies. Thus, the current work, performed by focusing on the EFP, successfully suggests a simplified yet efficient way to develop an indirect force estimation method in a nonlinear hydraulic mount as a dynamic load sensing system.

External force estimation for industrial robots using configuration optimization

External force estimation for industrial robots can be applied to the scenes such as human–robot interaction and robot machining. The model-based methods have gained the attention of many researchers because they only need motor signals. However, the performance of the above-mentioned methods depends on the accuracy of robot inverse dynamic model (IDM). Traditional methods focus on improving the accuracy of IDM to obtain a better estimation performance. However, suppressing the negative impact of modelling error can be achieved without reducing the modelling error. Thus, this article proposes an external force estimation method that uses the semi-parametric friction model and applies configuration optimization based on Jacobian condition number (JCN) to reduce the modelling error and its negative impact. First, the IDM is identified. Second, the semi-parametric friction model refines the traditional model in the torque observer to improve the estimation accuracy. Third, the JCN is used as an evaluating indicator to optimize the robot configurations. Finally, several simulations and experiments on a 6-DoF industrial robot demonstrate the validity of the proposed method. This method can enhance the performance of online force estimation by up to 48.28%. In addition, the application of the proposed method is verified on the laboratory-developed machining platform.

Radial Electromagnetic Force Estimation Using Strain Gauges in Switched Reluctance Motors

This study proposes a novel estimation method of the radial electromagnetic force in airgaps using strain gauges attached to the stator teeth in switched reluctance motors (SRMs). The proposed method compensates for the errors owing to magnetostriction and the non-uniform strain distribution on stator teeth. To verify the method, two test SRMs are fabricated with two different core materials; the 6.5% high silicon steel (10JNEX900) and amorphous iron (2605SA1), which have significantly different magnetostriction characteristics. Without the proposed compensations, the estimated radial force is higher than the finite element analysis (FEA) results owing to the extra deformation caused by magnetostriction. After applying the proposed method, the estimated force has good correspondence with the FEA results, and the error is reduced from 24% to 4% in the high silicon steel SRM. Regarding the amorphous iron SRM, the force estimated error was as high as 413% without compensations and is reduced to 64% by the proposed method. Additionally, the force waveforms are deformed because of the subtraction of the dominant magnetostrictive strain and phase delay in the amorphous iron SRM. In conclusion, the proposed method is effective in estimating the radial force on core materials with low magnetostriction.

Curvature-based force estimation for an elastic tube

AbstractContact force is one of the most significant feedback for robots to achieve accurate control and safe interaction with environment. For continuum robots, it is possible to estimate the contact force based on the feedback of robot shapes, which can address the difficulty of mounting dedicated force sensors on the continuum robot body with strict dimension constraints. In this paper, we use local curvatures to estimate the magnitude and location of single or multiple contact forces based on Cosserat rod theory. We validate the proposed method in a thin elastic tube and calculate the curvatures via Fiber Bragg Grating (FBG) sensors or image feedback. For the curvature feedback obtained from multicore FBG sensors, the overall force magnitude estimation error is$0.062 \pm 0.068$N and the overall location estimation error is$3.51 \pm 2.60$mm. For the curvature feedback obtained from image, the overall force magnitude estimation error is$0.049 \pm 0.048$N and the overall location estimation error is$2.75 \pm 1.71$mm. The results demonstrate that the curvature-based force estimation method is able to accurately estimate the contact force.

Mechanical Force Estimation Based on Visual Cell Deformation and Improved Point-Load Model

Measuring the mechanical force applied to a cell during penetration has long been studied. However, existing sensor methods can disturb or even prevent a planned cell surgery because of their physical units. We propose a contactless mechanical force estimation method based on an improved point-load model and cell contour detection. We extended the point-load model to large cell deformations and obtained a relationship between the mechanical force and the morphology of the deformed cell. We designed two cell contour detection methods for visually detecting the deformation of different cell morphologies to obtain the geometric parameters required by the model. Finally, we carried out penetration experiments on porcine oocytes and zebrafish embryos. The contour detection methods showed a consistency of up to 99.29% compared with manual marking, and the force estimation accuracy was 1.05 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{N}$ </tex-math></inline-formula> . We compared the estimation results of our method with measurements taken in real experiments and confirmed that our method accurately estimated the mechanical force applied to the cell without contact. Thus, it may be applied to estimating the applied force during cell surgery in real time.

Fingertip force estimation method of a human-like 3DOF robotic finger capable of fast tapping motion

Fingertip force estimation method of a human-like 3DOF robotic finger capable of fast tapping motion

High-accurate cutting forces estimation by machine learning with voice coil motor-driven fast tool servo for micro/nano cutting

In a machining process, cutting force is one of the most important machining parameters which can directly reflect the state of tool and quality of the machined workpiece. The cutting force in micro/nano cutting is required to be measured with a high accuracy of a few millinewtons. However, traditional cutting force indirect estimation methods are difficult to meet the high-accurate measurement requirement. In this paper, a reliable and high-accurate cutting force estimating method based on machine learning for a fast tool servo driven by Lorentz force without using additional sensors is proposed. A concept of virtual forces, which could enable obtaining sufficient type of forces than that by actual cutting, is developed to increase the data training accuracy. In addition, an effective data filtering method combining the fast Fourier transform and the inverse fast Fourier transform is adopted to further improve the estimation accuracy. Cutting force estimated by the proposed method is compared with that estimated by traditional disturbance observer and Kalman filter, which demonstrates a much better estimation accuracy. The experiment results show that the estimated cutting force is in good consistent with the best commercial dynamometer. Even the details of small cutting force fluctuation on the order of 50 mN can be clearly identified. The experimental results demonstrated that the cutting force estimation method for micro/nano machining process could have a high measurement resolution of 10 mN and relatively measurement error as small as 0.4%.

Novel Force Estimation Method for Magnetic Lead Screw-Based RotLin Actuator

This article proposes a method to precisely estimate the load force of magnetic lead screw (MLS)-based series elastic actuators (SEAs) in real time. The elastic transmissions of MLS-based SEAs generally comprise permanent magnets (PMs). Owing to the magnetic pole distortion and the characteristic nonlinear force of PMs, conventional force estimation methods are less accurate for MLS-based actuators than for SEAs with mechanical springs. In the proposed method, to precisely estimate force of MLSs, the nonlinear force characteristic of an MLS is modeled with a sinusoidal force model, and the magnetic force hysteresis phenomenon is predicted based on Bouc–Wen-type hysteresis equations. Moreover, a method called the relative displacement normalization method is proposed to detect the magnetic pole distortion and to compensate for the related force estimation error. In addition, the proposed method is applied to a rotary-linear (RotLin) actuator, which is a novel MLS-based linear SEA. The force estimation accuracy is experimentally evaluated by comparison with existing methods. The results demonstrate that the root-mean-square error of the proposed method is less than that of the existing polynomial model by up to 81.2%. Finally, a force controller and a static force control system is designed with the proposed model to prove its feasibility.