Contact Force Estimation Research Articles

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

Proprioceptive contact force and contact point estimation in a stationary snake robot

Measuring contact forces and knowing how and where a robot is interacting with obstacles in its environment is particularly useful for developing physics-based Obstacle-Aided Locomotion strategies for snake robots. The current paradigm for obtaining such measurements is mostly hardware-based, and is achieved through physical sensors that are attached to the outside of the chassis. Since external sensors are subject to wear and tear, it is in general preferrable to estimate external forces using solely sensors that may be hidden within the body of the robot. In this paper we contribute towards devising a method for performing such estimation tasks; more precisely, and building on the work of Liljebäsck et. al., we analyze the kinematics of the snake robot systems, and propose a method to estimate contact forces and contact points in a case where the robot remains stationary starting from proprioceptive measurements of constraint forces, accelerations, and force balance equations of a rigid body. The efficacy of the estimators in estimating contact point, contact force and direction is verified experimentally.

Event Augmentation for Contact Force Measurements

Neuromorphic vision sensor is an attractive technology that offers high dynamic range, and low latency which are crucial in robotic applications. However, the lack of event-based data in this field, limits the sensors’ performance in a real-world environments. In this paper, we propose a novel augmentation technique for neuromorphic vision sensors to improve contact force measurements from events. The proposed method shifts a proportion of events across the time domain, ’Temporal Event Shifting’, to augment the dataset. A new set of grasping experiments is performed to validate and analyze the effectiveness of the proposed augmentation method for contact force measurements. The results indicate that temporal event shifting is highly effective augmentation method which improves the models’ accuracy for the contact force estimation by thirty percent without performing new experiments.

Robot peg-in-hole assembly based on contact force estimation compensated by convolutional neural network

In order to reduce the cost of robot integrated application, this paper proposes a robot peg-in-hole assembly method without force/torque sensor. A disturbance observer based on generalized momentum and joint motor torque is employed to obtain the force information on the end of the robot, as a replacement of force/torque sensor. Since the contact between the robot end and the external environment excites the unmodeled dynamics of robot, which leads to insufficient estimation accuracy of the disturbance observer, a convolutional neural network (CNN) supervised learning method to calibrate the force estimation algorithm is proposed. Then, the peg-in-hole assembly mechanism of a ball-end round shaft is analyzed, and on this basis, an assembly strategy and a fuzzy proportional–derivative orientation adjustment algorithm are proposed. Finally, the feasibility of the proposed method of robot peg-in-hole assembly without force/torque sensor is verified by experiment. • Robot joint variables matrix forms a virtual two-dimensional image. • The convolutional neural network extracts features from robot joint variables matrix. • These features are mapped to the compensations of force estimation. • The compensated force estimation helps robot assembly without force sensor. • Robot peg-in-hole assembly based on fuzzy PD control is proposed.

Effectiveness of Global Optimisation and Direct Kinematics in Predicting Surgical Outcome in Children with Cerebral Palsy.

Multibody optimisation approaches have not seen much use in routine clinical applications despite evidence of improvements in modelling through a reduction in soft tissue artifacts compared to the standard gait analysis technique of direct kinematics. To inform clinical use, this study investigated the consistency with which both approaches predicted post-surgical outcomes, using changes in Gait Profile Score (GPS) when compared to a clinical assessment of outcome that did not include the 3D gait data. Retrospective three-dimensional motion capture data were utilised from 34 typically developing children and 26 children with cerebral palsy who underwent femoral derotation osteotomies as part of Single Event Multi-Level Surgeries. Results indicated that while, as expected, the GPS estimated from the two methods were numerically different, they were strongly correlated (Spearman’s ρ = 0.93), and no significant differences were observed between their estimations of change in GPS after surgery. The two scores equivalently classified a worsening or improvement in the gait quality in 93% of the cases. When compared with the clinical classification of responders versus non-responders to the intervention, an equivalent performance was found for the two approaches, with 27/41 and 28/41 cases in agreement with the clinical judgement for multibody optimisation and direct kinematics, respectively. With this equivalent performance to the direct kinematics approach and the benefit of being less sensitive to skin artefact and allowing additional analysis such as estimation of musculotendon lengths and joint contact forces, multibody optimisation has the potential to improve the clinical decision-making process in children with cerebral palsy.

Estimation of contact forces of granular materials under uniaxial compression based on a machine learning model

Estimation of contact forces of granular materials under uniaxial compression based on a machine learning model

Contact force estimation for serial manipulator based on weighted moving average with variable span and standard Kalman filter with automatic tuning

Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) is available. Consequently, the cost of collision recognition could be reduced dramatically.

Assessment of Different Mathematical Models for Analysis of Low-Velocity Impact on Composite Plates in Presence of Pre-loads

In this paper, the low-velocity impact response of composite plates in the presence of pre-loads is investigated using three new models for contact force estimation. The boundary conditions are considered as simply supported and the behavior of the material is linear elastic. The equations are based on both classical and first order shear deformation theory and the Fourier series method is used to solve the governing equations. The mass of the impactor is considered to be large mass and therefore the impact response is categorized as quasi-static. In the first impact model, the contact force history is first considered as a half-sine and then the maximum contact force and contact duration are calculated. In the second model, an improved two degree of freedom (ITDOF) spring-mass system is expressed by calculating the effective contact stiffness using a fast-iterative scheme. In the third model, which is expressed for the first time in this paper, the plate is considered as a series of masses and springs constructing a multi degree of freedom (MDOF) spring-mass system and the average forces applied to springs is introduced as the contact force. Validation of these models is done by comparing the results with the analytical, numerical and experimental results and shows good agreement. Results show that the new MDOF spring-mass system is more accurate for calculating the contact force rather than the ITDOF spring-mass system.

A learning-based tip contact force estimation method for tendon-driven continuum manipulator

Although tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

Robust Estimation of Contact Force and Location for Magnetic-Field-Based Soft Tactile Sensor Considering Magnetic Source Inconsistency.

Flexible magnetic-field-based tactile sensors (FMFTS) have numerous advantages including low cost, ease of manufacture, simple wiring, high sensitivity, and so on. Flexible magnetic-field-based tactile sensors need to be calibrated before use to build accurate mapping between contact force and magnetic field intensity measured by magnetic sensors; however, when considering remanence inconsistency of magnetic source, each FMFTS needs to be calibrated independently to enhance accuracy, and the complex preparation prevents FMFTS from being used conveniently. A robust estimation method of contact force and location that can tolerate remanence inconsistency of magnetic source in FMFTS is proposed. Firstly, the position and orientation of magnetic source were tracked using the Levenberg–Marquart algorithm, and the tracking results were insensitive to the remanence of magnetic source with appropriate cost function. Secondly, the mapping between magnitude and location of contact force and position and orientation of magnetic source was built with calibration of one sensor; the mapping only depends on the structural response of flexible substrate, and thus can be extended to estimate external force and location for other sensors with the same structure. The proposed method was evaluated in both simulations and experiments, and the results confirm that the estimation of magnitude and location of external force for FMFTS with the same structure and different remanence could reach acceptable accuracy, depending on single calibration. The proposed method can be used to simplify the calibration procedure and remove the barrier for large-scale application of FMFTS and replacement of damaged FMFTS.

Generalized EMG-based isometric contact force estimation using a deep learning approach

EMG-based force estimation is generally done in a subject specific manner. In this paper, we explore force estimation in a manner generalizable across individuals, where the EMG signals are recorded from the long head and the short head of biceps brachii, and the brachioradialis, under isometric elbow flexion, while the contact force is measured at the wrist. Deep convolutional neural networks (CNN), which utilize feature-level fusion of representations learned from high-density (HD) EMG in the time and frequency domains, are developed. The performance of the proposed solution (CNN-FLF) is compared to a number of baselines including CNNs with input-level fusion of HD-EMG data in the time and frequency domains, CNNs which estimate force from time or frequency domain EMG data separately, and a number of classical machine learning methods, which use hand-crafted features extracted from the EMG signals. Results show that the CNN-FLF, with optimized hyper-parameters, outperformed all other methods, giving a normalized mean squared error for estimated force of 1.6±3.69% (mean±SD). In visualization of the extracted features for the different CNN models, it is apparent that the final features of the CNN-FLF enable finding an accurate regressive relationship with output force levels. • First study to use feature-level fusion of EMG in different domains to estimate force • The model estimates force in an inter-subject manner • Outperforms baselines including single-domain data and classical machine learning methods

Piezoresistive textile layer and distributed electrode structure for soft whole-body tactile skin

Tactile sensors based on electrical resistance tomography (ERT) provide pressure sensing over a large area using only a few electrodes, which is a promising property for robotic tactile skin. Most ERT-based tactile sensors employ electrodes only on the sensor’s edge to avoid undesirable artifacts caused by electrode contact. The distribution of these electrodes is critical, as electrode location largely determines the sensitive regions, but only a few studies have positioned electrodes in the sensor’s central region to improve the sensitivity. Establishing the use of internal electrodes on a stretchable textile needs further investigation into piezoresistive structure fabrication, measurement strategy, and calibration. This article presents a comprehensive study of an ERT-based tactile sensor with distributed electrodes. We describe key fabrication details of a layered textile-based piezoresistive structure, an iterative method for choosing the current injection pathways that yields pairwise optimal patterns, and a calibration process to account for the spatially varying sensitivity of such sensors. We demonstrate two sample sensors with electrodes located only on the boundary or distributed across the surface, and we evaluate their performance via three methods widely used to test tactile sensing in biological systems: single-point localization, two-point discrimination, and contact force estimation.

Stator-Rotor Contact Force Estimation of Rotating Machine

In turbomachines, dry friction resulting from stator–rotor contacts is a severe problem that may degrade lifetime of the machine or even lead to complete failure. Knowledge about the system states and contact forces is beneficial for system monitoring or to prevent contacts through, e.g., active magnetic bearings. In this paper, a nonlinear model is derived that describes the lateral rotor vibrations in the case of contact and no contact. The elastic behavior of the shaft is modeled based on the finite-element method. The contact is described by a dry friction model. An augmented system description is formulated that allows estimation of rotor displacements and contact forces by means of nonlinear filtering approaches like an extended Kalman filter. A simulation study was conducted that explicitly considered the hazardous backward whirl. The suggested approach shows suitable estimation performance related to both state and contact force estimation.

Profile and contact force estimation of cable-driven continuum robots in presence of obstacles

• A new optimization-based framework for contact estimation of cable-driven continuum robots (CCR). • Determination of contact location from the Lagrange multipliers . • Estimation of contact forces using virtual work principle and the developed kinematics framework. • Validation of numerically obtained profile and contact forces with experiments on a 3D printed CCR. • Extension to profile estimation in 3D. Accurate prediction of shape and contact forces significantly improves the performance of a continuum robot during its operation in obstacle-laden environments. This paper presents an optimization-based mathematical framework to predict the bending profile of a cable-driven continuum robot in presence of obstacles. The kinematics model is derived from the concept of strain energy minimization and can easily incorporate obstacles as inequality constraints in the optimization-based approach. The location of point of contact can be identified by observing the Lagrange multipliers of the inequality constraints. Using the kinematics model and the principle of virtual work, a method to estimate the reaction forces at contact is proposed. The model shows high accuracy, with RMS error of 1.35 mm in prediction of the pose for experiments conducted on a 180 mm long robot prototype. Validation experiments are also conducted on the prototype by imposing contact at different locations on the robot. In all cases, the average error in predicting the contact force is found to be less than 1.0 g for applied loads ranging from 50 to 350 g.

Identification of Contact Position and Force Estimation on Manipulator Using Force Sensor Implemented on Base Frame

Identification of Contact Position and Force Estimation on Manipulator Using Force Sensor Implemented on Base Frame

Towards automated ultrasound imaging—robotic image acquisition in liver and prostate for long-term motion monitoring

Real-time volumetric (4D) ultrasound has shown high potential for diagnostic and therapy guidance tasks. One of the main drawbacks of ultrasound imaging to date is the reliance on manual probe positioning and the resulting user dependence. Robotic assistance could help overcome this issue and facilitate the acquisition of long-term image data to observe dynamic processes in vivo over time. The aim of this study is to assess the feasibility of robotic probe manipulation and organ motion quantification during extended imaging sessions. The system consists of a collaborative robot and a 4D ultrasound system providing real-time data access. Five healthy volunteers received liver and prostate scans during free breathing over 30 min. Initial probe placement was performed with real-time remote control with a predefined contact force of 10 N. During scan acquisition, the probe position was continuously adjusted to the body surface motion using impedance control. Ultrasound volumes, the pose of the end-effector and the estimated contact forces were recorded. For motion analysis, one anatomical landmark was manually annotated in a subset of ultrasound frames for each experiment. Probe contact was uninterrupted over the entire scan duration in all ten sessions. Organ drift and imaging artefacts were successfully compensated using remote control. The median contact force along the probe’s longitudinal axis was 10.0 N with maximum values of 13.2 and 21.3 N for liver and prostate, respectively. Forces exceeding 11 N only occurred in 0.3% of the time. Probe and landmark motion were more pronounced in the liver, with median interquartile ranges of 1.5 and 9.6 mm, compared to 0.6 and 2.7 mm in the prostate. The results show that robotic ultrasound imaging with dynamic force control can be used for stable, long-term imaging of anatomical regions affected by motion. The system facilitates the acquisition of 4D image data in vivo over extended scanning periods for the first time and holds the potential to be used for motion monitoring for therapy guidance as well as diagnostic tasks.