Contact Force Estimation Research Articles

A multi-asperity adhesive contact model for catheter and vascular artery contact in endovascular surgery.

Contact behaviors of medical devices, such as guidewires and catheters, are critical in endovascular surgeries. In this work, a new method to predict adhesive contact force between catheter and vascular artery is presented. Multi-asperity adhesion on the surface of vascular artery, deformation of asperity and deformation of vascular substrate are all considered. The single asperity behavior is described with Johnson-Kendall-Roberts (JKR) contact model. The multi-asperity behavior is based on Greenwood-Williamson (GW) asperity model. Vascular substrate is considered as elastic bulk substrate and its deformation is determined with Hertzian pressure from asperity on a circular region on the elastic half space. The model shows that the deformation of vascular substrate accounts for the majority of the total contact deformation and significantly affects the predicted contact force. The model is verified with published experimental data. The comparison shows that the model produces very accurate prediction of contact force between catheter and vascular artery when the contact force is compressive. Parametric analysis based on asperity topography is carried out. The analysis shows that the diameter of the circular region of the interface between asperity and vascular substrate has more significant effect on the estimation of contact force than the radius of asperity. Further validation of prediction accuracy of the model under experiment is needed.

Continuous deformation analysis and contact force estimation for pneumatic bending actuators interacting with environment

Soft bending actuators show high adaptability for applications such as rehabilitation or grasping. Although constant curvature assumption has been extensively used for free motion modeling, these actuators do not bend circularly when interacting with the environment. In such situation, conventional bending sensors cannot provide useful information on their shape. In this paper, the Finite Rigid Elements approach is utilized to model the behavior of a soft pneumatic bending actuator in free motion and contact. With this method, the variable curvature configuration under different external loads can be modeled. Then, the contact force between the actuator and an object located in a specific position is estimated utilizing the Gradient Descent optimization method. Experimental results verified the combinatorial proposed approach for both force estimation and structure deformation.

Design of a Compliant Robotic End-Effector Tool for Normal Contact Estimation

The use of industrial robots for machining operations such as polishing is gaining popularity these days. However, maintaining constant normal contact on unknown geometrical surfaces still remains a challenge. In order to cope with the issue, this research has developed a unique compliant robotic end-effector that can measure the depth and angles deviation of the robot from the normal complex surfaces in real-time without any prior geometric model of the workpiece. The indigenously developed compliant robotic end-effector is made up of three main components: a novel three-axis force/torque sensor, a compliant contact pin, and a data acquisition (DAQ) board. Initially, the three-axis force/torque sensor has been validated with both simulation and testing. The advantage of being able to take measurements in real-time is provided by the DAQ’s automatic weight compensation feature. Additionally, the pose correction algorithm receives the estimated contact force as feedback during robot surface tracking in order to assess depth and angles and determine the proper distance and perpendicular relationship between the surface and the end-effector. The proposed scheme is then evaluated using a six-axis industrial robot. The experimental results show that the maximum average depth and angle errors are 0.13 mm and 0.64°, respectively. The developed system has achieved significant improvement in contact-based robot surface measuring.

Improved estimation of rail–wheel contact forces from instrumented wheel-set data through higher harmonic cancellation and a back-propagation neural network scheme

Instrumented wheelsets, equipped with numbers of strain gauges, are widely used to estimate the rail–wheel contact parameters. The accuracy in estimation of rail–wheel contact parameters from the measured strain signals depends on the layout of the strain gauges and correlation techniques adopted for processing measured signals. Harmonic components estimation is the most widely used conventional correlation technique. However, its accuracy in estimation of rail–wheel contact parameters is limited to the order of 80%–90%. This study proposes a signal modification algorithm, initially utilizing cancellation of next significant higher harmonics than those considered in the existing algorithms and then further, a novel Artificial Neural Network architecture for the same purpose.The signal modification approach is based on the realization that the seventh harmonic has significant contribution on the strain signals. The proposed algorithm is aimed at estimating and cancelling the effects of the seventh harmonic in strain signals. The accuracy thus improves to 91% in vertical contact force estimates, while the accuracy in estimation of lateral contact force and lateral contact position are improved to 97% and 95%, respectively. Due to the highly non-linear nature of the rail–wheel contact parameters, an optimized nonlinear mapping is required for further improvement in the accuracy.The viability of using artificial neural networks in estimation of rail–wheel contact parameters have been explored earlier. The proposed architectures are complicated and require extensive manual optimization of tuning parameters. A simple back-propagation artificial neural network architecture, using Bayesian regularization training function with Levenberg–Marquardt optimization technique is proposed in this study. The proposed neural network consists of 18 measured strain signals as input, three hidden layers with 20, 6 and 3 neurons, respectively, and vertical force, lateral force and lateral contact position as three outputs. Accuracies of the order of 99%, 99% and 96% are achieved in the estimation of vertical contact force, lateral contact force and lateral contact position respectively. The input and training data is generated from the multi-body dynamics and finite element simulations.

Estimation and Comparison of Knee Joint Contact Forces During Heel Contact and Heel Rise Deep Squatting.

Increased knee flexion is required for deep squatting in the daily life of the non-western population as well as in many sports activities. The purpose of this study was to estimate as well as to compare knee joint contact forces during heel contact (HC) and heel rise (HR) deep squatting in 10 healthy young Indian participants. Kinematic data were captured using a 12-camera Motion Analysis system. Kinetic data were collected using two Kistler force plates. EMG of 6 lower limb muscles was monitored by Noraxon wireless EMG. OpenSim musculoskeletal model was customized to increase the maximum knee flexion capability of the existing model and knee joint contact forces were estimated. A significant difference in tibiofemoral (p < 0.001) as well as patellofemoral (p = 0.006) knee joint contact force was observed between HC and HR squatting. The resultant maximum tibiofemoral KJCF was 5.9 (± 0.54) times body weight (BW) and 5.3 (± 0.6) BW for the HC and HR, respectively. The resultant maximum patellofemoral KJCF was 7.8 (± 0.57) BW and 7.1 (± 0.73) BW for the HC and HR, respectively. The findings can provide implications for physiotherapists to design rehabilitation exercise protocols, exercise professionals, and the development of high flexion knee implants. The online version contains supplementary material available at 10.1007/s43465-022-00798-y.

Stochastic model-based contact force estimation for concentric tube robots

This work utilizes a Wiener process to describe a fluctuating load on a Concentric tube robot (CTR) or its smooth motion through fixed obstacles leading to a kineto-static piecewise constant strain (KPCS) model. We then propose an observer based on the KPCS model, represented as a stochastic switched system, to estimate the externally applied forces by knowing the position of discrete points on the backbone. We have demonstrated that the observer describes the behavior of CTRs in unknown constraint environments and slow movement. The experiments carried out using electromagnetic sensors on the CTR prototype validate the feasibility of the proposed approach for shape and tip-force estimation. The average time required for force estimation is about 3.8, 6.2, and 20 ms per sample, respectively, when the estimated external forces are applied on a three-tube CTR’s tip point and three and fifteen equally spaced points along its length. The implementation results reveal the estimated force reduces the error of the robot tip position by half (about 1.5% of the robot length on average) compared to the known external force.

Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation.

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue's stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.

3D Contact Point Cloud Reconstruction From Vision-Based Tactile Flow

With the growing interest in vision-based tactile sensors, various types of sensors that utilize digital imaging are being developed. Among them, a group of sensors captures the tactile flow resulted from the contact deformation using the optical flow algorithm from computer vision and achieves full resolution deformation tracking on the tactile surface. In this work, a novel 3D contact reconstruction algorithm is proposed and evaluated. It exploits the contact geometry and projection relationship in the tactile flow, which are versatile for vision-based tactile sensors, unique for tactile perception but not inherited from computer vision. The resulted 3D contact point cloud representation is consistent with the tactile flow constraint, scale estimation, and contact edge estimation. It can be directly manipulated in downstream applications such as contact force estimation and contact pose estimation. Experiments and examples are provided that indicate the potential for the proposed tactile processing algorithm to connect tactile perception to tactile enabled robotic manipulation tasks.

A Soft Barometric Tactile Sensor to Simultaneously Localize Contact and Estimate Normal Force With Validation to Detect Slip in a Robotic Gripper

Soft tactile sensing technologies provide the potential to endow a sense of touch to robots for grasping and manipulating objects. During the execution of such tasks, having accurate knowledge on the contact location and quantitative forces in broad exerted force ranges is key. For industrial adoption such sensor needs to be low-cost, robust with limited or no calibration procedures and easy to manufacture. In this work we present a microelectromechanical (MEMS) based barometric sensor array covered with an elastomer layer, with the sensor signals being interpreted in real-time on the basis of a parameterized Gaussian type of distribution. The contact location is determined by finding in real-time the corresponding parameters of the Gaussian distribution that on its turn is used for normal contact force estimation. Results show accuracies in terms of localization of 0.5 mm and normal force errors of 10 % in force ranges up to 25 N and 15 % in high force ranges of 25–50 N. The proposed soft tactile sensor has furthermore been validated to provide the ability to detect slip when gripping various objects.

Variable stiffness locomotion with guaranteed stability for quadruped robots traversing uneven terrains.

Quadruped robots are widely applied in real-world environments where they have to face the challenges of walking on unknown rough terrains. This paper presents a control pipeline that generates robust and compliant legged locomotion for torque-controlled quadruped robots on uneven terrains. The Cartesian motion planner is designed to be reactive to unexpected early and late contacts using the estimated contact forces. Moreover, we present a novel scheme of optimal stiffness modulation that aims to coordinate desired compliance and tracking performance. It optimizes joint stiffness and contact forces coordinately in a quadratic programming (QP) formulation, where the constraints of non-slipping contacts and torque limits are imposed as well. In addition, the issue of stability under variable stiffness control is solved by imposing a tank-based passivity constraint explicitly. We finally validate the proposed control pipeline on our quadruped robot CENTAURO in experiments on uneven terrains and, through comparative tests, demonstrate the improvements of the variable stiffness locomotion.

Estimation of tire-road contact forces through a model-based approach employing strain measurements

The employment of intelligent tires to test the vehicle performances is ever-increasing in the last years. Many research activities have been made to correlate measurements provided by sensors to the tire dynamics. In this paper, a novel tire-road forces estimation technique is presented. The developed estimator is based on an approximation of the well-known Flexible Ring Tire Model (FRTM) and therefore, it is suitable for real-time normal and tangential forces identification. Only the tread band strain measurements are employed in the developed algorithm which can estimate the tire-road forces at every wheel revolution. The tool is validated through both numerical and experimental tests. The results indicate that the developed estimation algorithm, obtained by a mathematical inversion of the FRTM, can be employed as a monitoring tool for tires and vehicles.

Electromyography measurements of the deep hip muscles do not improve estimates of hip contact force

The deep hip muscles are often omitted in studies investigating hip contact forces using neuromusculoskeletal modelling methods. However, recent evidence indicates the deep hip muscles have potential to change the direction of hip contact force and could have relevance for hip contact loading estimates. Further, it is not known whether deep hip muscle excitation patterns can be accurately estimated using neuromusculoskeletal modelling or require measurement (through invasive and time-consuming methods) to inform models used to estimate hip contact forces. We calculated hip contact forces during walking, squatting, and squat-jumping for 17 participants using electromyography (EMG)-informed neuromusculoskeletal modelling with (informed) and without (synthesized) intramuscular EMG for the deep hip muscles (piriformis, obturator internus, quadratus femoris). Hip contact force magnitude and direction, calculated as the angle between hip contact force and vector from femoral head to acetabular center, were compared between configurations using a paired t-test deployed through statistical parametric mapping (P < 0.05). Additionally, root mean square error, correlation coefficients (R2), and timing accuracy between measured and modelled deep hip muscle excitation patterns were computed. No significant between-configuration differences in hip contact force magnitude or direction were found for any task. However, the synthesized method poorly predicted (R2-range 0.02–0.3) deep hip muscle excitation patterns for all tasks. Consequently, intramuscular EMG of the deep hip muscles may be unnecessary when estimating hip contact force magnitude or direction using EMG-informed neuromusculoskeletal modelling, though is likely essential for investigations of deep hip muscle function.

A cable driven robotic palpation system with contact force sensing based on cable tension observation.

Prostate Cancer screening based on manual palpation is subjective. Robotic palpation systems can objectively acquire stiffness conditions of the prostate. A 2DoF cable driven robotic system for prostate palpation is proposed. An indirect method to estimate the contact force based on cable tension observation is presented. Kinematic models and a joint angle estimation method to determine the tip position of the probe are derived. Positioning accuracy was verified using an optical marker tracking system and by displacement measurement, respectively. The contact force estimation method was validated on silicone phantom samples. A good consistence between the estimated and measured contact force was observed. The contact force was correlated with the elastic modulus of each silicone phantom. There was also a good agreement between the theoretical and the measured tip position. In the proposed palpation system, the indirect contact force estimation method is viable and holds potential for the stiffness assessment of the prostate. The tip position vital for palpation can be determined through estimated joint angles.

Contact Localization of Continuum and Flexible Robot Using Data-Driven Approach

Continuum robots such as robotic catheters are increasingly being used in minimally invasive surgery. Compliance contributes to enhanced safety during e.g. catheter insertion, however, estimation of contact force and location may help clinicians avoiding exerting excessive force. Ultimately this could lead to faster and safer interventions. Researchers proposed force sensors integrated in the catheter tip in the past. However, such sensors add extra complexity to the catheter design. Also, tip force sensors do not provide insights on forces that act along the catheter length. This paper proposes a data-driven approach for localizing contact forces that appear over the length of the catheter. The proposed approach consists of a collision detection method and a contact localization method. The framework only requires the measurement of the catheter’s shape which can be done by an embedded multi-core Fiber Bragg Grating fiber. The method was validated experimentally with a 3D-printed continuum robot with an integrated multi-core fiber. A second contact localization method which is based on identifying the discontinuity in the measured curvature, is also implemented and compared with the proposed method. The static and dynamic experiments show a mean average localization error of 2.3 mm and 4.3 mm which correspond to respectively 3.3% and 6.1% of a 70 mm long flexible robot. These findings demonstrate that the proposed framework outperforms the previous methods and yield promising results. The contact state estimation algorithm can detect collisions in at most approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.08}\,\text{s}$</tex-math></inline-formula> .

Estimation of Tibiofemoral Joint Contact Forces Using Foot Loads during Continuous Passive Motions.

Continuous passive motion (CPM) machines are commonly used after various knee surgeries, but information on tibiofemoral forces (TFFs) during CPM cycles is limited. This study aimed to explore the changing trend of TFFs during CPM cycles under various ranges of motion (ROM) and body weights (BW) by establishing a two-dimensional mathematical model. TFFs were estimated by using joint angles, foot load, and leg–foot weight. Eleven healthy male participants were tested with ROM ranging from 0° to 120°. The values of the peak TFFs during knee flexion were higher than those during knee extension, varying nonlinearly with ROM. BW had a significant main effect on the peak TFFs and tibiofemoral shear forces, while ROM had a limited effect on the peak TFFs. No significant interaction effects were observed between BW and ROM for each peak TFF, whereas a strong linear correlation existed between the peak tibiofemoral compressive forces (TFCFs) and the peak resultant TFFs (R2 = 0.971, p < 0.01). The proposed method showed promise in serving as an input for optimizing rehabilitation devices.

Implementation of sensorless contact force estimation in collaborative robot based on adaptive third-order sliding mode observer

In this paper, we propose the estimation of contact force in a collaborative robot without explicit force-sensing based on the adaptive third-order sliding mode observer. An adaptive third-order sliding mode observer was designed to estimate the contact force based only on the position measurement. The information from the observer was used for admittance control and emergent stop control when the robot interacts with a human. The experimental results with the emergent stop control and admittance control, using the proposed contact force estimation for the 3-DOF AT2-FARA robot manipulator, illustrate the capability of the proposed system in real-world application.

Variable Stiffness Shoes for Knee Osteoarthritis: An Evaluation of 3-Dimensional Gait Mechanics and Medial Joint Contact Forces.

The study aim was to quantify the impact of a commercially available variable stiffness shoe (VSS) on 3-dimensional ankle, knee, and hip mechanics and estimated knee contact forces compared with a control shoe. Fourteen participants (10females) with knee osteoarthritis completed gait analysis after providing informed consent. Shoe conditions tested were control shoe (New Balance MW411v2) and VSS (Abeo SMART3400). An OpenSim musculoskeletal model with static optimization was used to estimate knee contact forces. There were no differences in joint kinematics or in the knee adduction or flexion moments (P = .06; P = .2). There were increases in the knee internal and external rotation (P = .02; P = .03) and hip adduction and internal rotation moments for VSS versus control (P = .03; P = .02). The estimated contact forces were not different between shoes (total P = .3, medial P = .1, and lateral P = .8), but contact force changes were correlated with changes in the knee adduction moment (medial r2 = .61; P < .007). High variability in knee flexion moment changes and increases in the internal rotation moment combined with small decreases in the knee adduction moment did not lead to decreases in estimated contact forces. These results suggest that evaluation of VSS using only the knee adduction moment may not adequately capture its impact on osteoarthritis.

Unified Method for Task-Space Motion/Force/Impedance Control of Manipulator With Unknown Contact Reaction Strategy

Nowadays, higher requirements are placed on the design of the controller when robots enter the factory and co-operate with human. In this letter, we propose a unified task space control method combined with the contact reaction strategy in null space of redundant manipulator, which can not only conveniently switch between different control objectives according to specific tasks, but also react to the unknown contact with compliance. Specifically, the unknown contact force applied on the manipulator’s body is online estimated based on recursive least square estimation law in joint space. Then, the closed-loop dynamics of different control objectives are unified in task space by a generalized target model, and a model-reaching law based on adaptive robust control is proposed to achieve the target model even if there exists an unknown contact. Meanwhile, the estimated contact force is not only compensated in the model-reaching control law to guarantee the correct task execution but also used for the null space impedance control design to ensure compliant behavior. The performance and stability of the proposed scheme are guaranteed and proved in theory. Experiments are conducted on a 7-DoF manipulator for the validation.

Soft Resistive Tactile Sensor Based on CNT-PDMS-Gel to Estimate Contact Force

Soft tactile sensors have been extensively studied due to their promising potential in estimating applied forces while exhibiting mechanical compliance. Such sensors have been fabricated from a variety of composite materials and various techniques to advance their applications. Currently, the development of soft tactile sensors based on carbon nanotubes (CNT)–polydimethylsiloxane (PDMS) gel has yet been investigated. This letter presents the design of a soft tactile sensor based on a CNT-–PDMS-gel composite that is sensitive to external forces. The sensor was composed of an elastomer, a rectangular conductive body made from a PDMS–CNT-gel composite, and a circuit board. The electrical resistance of the composite changed in response to the strain applied. Push and release tests were conducted, and the conductive body with 1% CNT was chosen due to high sensitivity, fast response, and stability in reading output. Experimental results, using a feed-forward neural network, showed that the sensor can estimate the applied normal force based on the changes of resistance.

Estimation of contact force from grinding sound based on frequency analysis and machine learning

Estimation of contact force from grinding sound based on frequency analysis and machine learning