Contact Force Estimation Research Articles

Motion planning and searching strategy of a transverse ledge climbing robot based on force feedback

Abstract A transverse ledge climbing robot inspired by athletic locomotion is a customized robot aiming to travel through horizontal ledges in vertical walls. Due to the safety issue and complex configurations in graspable ledges such as horizontal, inclined ledges, and gaps between ledges, existing well-known vision-based navigation methods suffering from occlusion problems may not be applicable to this special kind of application. This study develops a force feedback-based motion planning strategy for the robot to explore and make feasible grasping actions as it continuously travels through reachable ledges. A contact force detection algorithm developed using a momentum observer approach is implemented to estimate the contact force between the robot’s exploring hand and the ledge. Then, to minimize the detection errors due to dynamic model uncertainties and noises, a time-varying threshold is integrated. When the estimated contact force exceeds the threshold value, the robot control system feeds the estimated force into the admittance controller to revise the joint motion trajectories for a smooth transition. To handle the scenario of gaps between ledges, several ledge-searching algorithms are developed to allow the robot to grasp the next target ledge and safely overcome the gap transition. The effectiveness of the proposed motion planning and searching strategy has been justified by simulation, where the four-link transverse climbing robot successfully navigates through a set of obstacle scenarios modeled to approximate the actual environment. The performance of the developed grasping ledge searching methods for various obstacle characteristics has been evaluated.

High-Bandwidth Contact State Estimation with only Joint Angle Feedback for Legged Robots

Considering the contact between robot feet and the ground is unpredictable in unstructured terrains, robots require fast and accurate contact detection capability. This paper presents a contact detection method through estimating external force that benefits from compensating friction torque and driving torque effectively. For estimating external force, a new estimation method for joint angular velocity/acceleration is proposed. Furthermore, filter parameters are selected theoretically based on the leg control model, which achieves the optimal solution of the parameters. A control compensation strategy of the low-velocity zone of the motor is also implemented for optimizing the estimation of contact force. In contrast to the classic generalized momentum method, the proposed method allows high bandwidth estimation. The proposed contact detection method and the angular velocity/acceleration estimation method using only discrete position feedback information are verified on the developed quadruped robot platform SCIT Dog.

Hardware Implementation for Triaxial Contact-Force Estimation from Stress Tactile Sensor Arrays: An Efficient Design Approach.

This paper presents a contribution to the state of the art in the design of tactile sensing algorithms that take advantage of the characteristics of generalized sparse matrix-vector multiplication to reduce the area, power consumption, and data storage required for real-time hardware implementation. This work also addresses the challenge of implementing the hardware to execute multiaxial contact-force estimation algorithms from a normal stress tactile sensor array on a field-programmable gate-array development platform, employing a high-level description approach. This paper describes the hardware implementation of the proposed sparse algorithm and that of an algorithm previously reported in the literature, comparing the results of both hardware implementations with the software results already validated. The calculation of force vectors on the proposed hardware required an average time of 58.68 ms, with an estimation error of 12.6% for normal forces and 7.7% for tangential forces on a 10 × 10 taxel tactile sensor array. Some advantages of the developed hardware are that it does not require additional memory elements, achieves a 4× reduction in processing elements compared to a non-sparse implementation, and meets the requirements of being generalizable, scalable, and efficient, allowing an expansion of the applications of normal stress sensors in low-power tactile systems.

End-effector contact force estimation for the industrial robot in automated fiber placement processes with dynamic end-load variations

Contact force estimation makes the interaction between the robot and the environment perceptible and controllable without the need for additional sensor devices. For robots used in Automated Fiber Placement (AFP), contact force estimation is primarily aimed at grasping the magnitude and real-time variations of the compaction roller force, which is one of the most critical process parameters in AFP, directly related to layup accuracy and quality. However, contact force estimation during AFP faces challenges: the consumable carbon fiber prepreg material is mounted on the end-effector, leading to continuous changes in load dynamics parameters. Ignoring this situation renders contact force estimation unreliable, making real-time end-load calibration necessary. This paper first establishes a linear dynamic model of the robot and obtains the Variable Parameter Set (VPS) related to end-load. Then, combining distance sensors and weighted moving average, the volume changes in material rollers are measured to perform fast load calibration of the end-effector and update the VPS. Subsequently, a joint hybrid friction model is established and calibrated. Based on this, a Disturbance Kalman Filter (DKF) observer is established using inverse dynamics and discrete Kalman filter algorithms for real-time estimation of contact force. The calibration routine of the DKF observer considers the noise in measured motor currents and errors in velocity data from individual joints. Vertical lifting experiments and actual AFP experiments demonstrate that fast load calibration and DKF observer can provide robust and accurate estimation of end-effector contact force. Compared to force sensors and the DKF observer without FLC, the RMSE is reduced by over 49 %.

Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

Inspired by Model Predictive Interaction Control (MPIC), this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact tasks. Within the MPIC, robot dynamics are linearized, and Extended Kalman Filters are used for the online estimation of geometry-aware parameters. Meanwhile, a geometry-aware Hertz contact model is introduced for the online estimation of contact forces. We then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization MPC. Experimental validations were designed for two contact modes: “single-point contact” and “continuous contact”, involving materials with four different Young’s moduli and tested in human arm “relaxation-contraction” task. Results indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee safety. Our method reduces the maximum impact force by 50% and decreases the average force error by 42%. The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid, soft, and deformable objects.

Force control for the robot-assisted laparoscopic ultrasound scanning system

Laparoscopic ultrasound scanning, an acritical diagnostic imaging method for liver diseases, necessitates precise contact force control that is typically dependent on skilled physicians. Given its high mobility and controllability, the robot is anticipated to supplant physicians in performing laparoscopic ultrasound scans. Consequently, this study introduces a laparoscopic ultrasound scanning system paired with a learning-based force controller. Compared with traditional methods, our approach offers precise control over the contact force between the ultrasound probe and human organ surfaces during scanning, reducing the learning curve for surgeons in laparoscopic liver ultrasound scanning. Acknowledging the complex nonlinear mechanical model involved when the laparoscopic ultrasound probe contacts human organs and tissues, we propose a learning-based method utilizing a long short-term memory network to estimate the contact force. This method allows for the precise estimation of contact force with human organ tissues by analyzing tendon force. The results indicate that the proposed LSTM neural network can accurately fit the mechanical model of the robotic catheter, with a root mean square error of 3.44 mN. Finally, we conducted a force control experiment for ultrasound scanning on a silicone liver model. The results indicate that the proposed method can achieve relatively stable force control for laparoscopic ultrasound scanning, with a maximum error of 2.41 mN during the transient phase of system control and a maximum absolute error of 1.8 mN in the steady state.

Study on intelligent tyre force estimation algorithm based on strain analysis

The objective of this paper is to propose a novel approach to develop an algorithm for estimating the grounding force between a tyre and the road surface. First, a finite element model of a heavy-duty tyre is developed in this paper, and the validity of the model is verified by load application tests and mechanical vibration tests. When the tyres are operated under various operating conditions, that is, load, side deflection, side tilt and longitudinal slip conditions, the sensitivity analysis is performed by combining the three-directional forces based on the strain curves measured when the tyres are rolling, respectively. In order to achieve accurate estimation of the three-directional forces, this paper establishes a three-directional force estimation model using the support vector machine (SVM) technique, with the tyre force-sensitive eigenvalues as inputs and the tyre forces as outputs, so as to achieve the three-directional force estimation of the tyre-grounded three-directional forces. joint estimation of tyre-road contact force. The results show that the estimation error of vertical force is 1.79%, the estimation error of lateral force is 3%, the estimation error of longitudinal force is 4.65% and in general, the estimation error of grounding force is kept within 5%, and the estimation effect is good.

Dynamic Contact Force Estimation via Integration of Soft Sensor Based on Fiber Bragg Grating and Series Elastic Actuator

Dynamic Contact Force Estimation via Integration of Soft Sensor Based on Fiber Bragg Grating and Series Elastic Actuator

Utilizing on-board sensing of passing train vehicles for virtual sensing of bridges

This study proposes a bridge response reconstruction approach using exclusively measurements obtained from instrumented train vehicles with known properties. The approach relies on an Augmented Kalman Filter (AKF) technique to estimate contact forces without prior knowledge of rail profile irregularities. Furthermore, the proposed methodology addresses the challenge of accurate vehicle-bridge interaction (VBI) contact force estimation in the presence of rigid-body modes in the train model, by separating rigid-body from flexible modes, and establishing a state-space formulation that incorporates the vehicle dynamics of solely flexible modes. The study examines, by means of numerical simulations, the impact of measurement and model errors, vehicle speed, and rail irregularities on both contact force estimation and bridge response reconstruction. It also investigates sensor configurations that minimize the number of sensors required on the train and shows that acceleration at all wheels and strain of at least one primary suspension per bogie are necessary for accurate force estimation. The results emphasize the significant role of VBI in achieving accurate bridge response reconstruction. The estimated contact forces in combination with a bridge model allow the reconstruction of the bridge response through virtual sensing, providing valuable information on the bridge’s health status.

An Efficient Motion Adjustment Method for a Dual-Arm Transfer Robot Based on a Two-Level Neural Network and a Greedy Algorithm

As the manipulation object of a patient transfer robot is a human, which can be considered a complex and time-varying system, motion adjustment of a patient transfer robot is inevitable and essential for ensuring patient safety and comfort. This paper proposes a motion adjustment method based on a two-level deep neural network (DNN) and a greedy algorithm. First, a dataset including information about human posture and contact forces is collected by experiment. Then, the DNN, which is used to estimate contact force, is established and trained with the collected datasets. Furthermore, the adjustment is conducted by comparing the estimated contact force of the next state and the real contact force of the current state by a greedy algorithm. To assess the validity, first, we employed the DNN to estimate contact force and obtained the accuracy and speed of 84% and 30 ms, respectively (implemented with an affordable processing unit). Then, we applied the greedy algorithm to a dual-arm transfer robot and found that the motion adjustment could reduce the contact force and improve human comfort efficiently; these validated the effectiveness of our proposal and provided a new approach to adjust the posture of the care receiver for improving their comfort through reducing the contact force between human and robot.

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces.

The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.

Unified force-impedance control

Unified force-impedance control (UFIC) aims at integrating the advantages of impedance control and force control. Compliance and exact force regulation are equally important abilities in modern robot manipulation. The developed passivity-based framework builds on the energy tank concept and is suitable for serial rigid and flexible-joint robots. Furthermore, it is able to deal either with direct force measurements or model-based contact force estimation. Thus, in this theoretical framework, the most relevant practical systems are covered and shown to be stable for arbitrary passive environments. Particular focus is also laid on a robust impedance-based contact/non-contact stabilization methodology that prevents abrupt, unwanted, and potentially dangerous movements of the manipulator in case of contact loss, a well-known problem of both impedance and force control. The validity of the approach is shown in simulation and through various experiments. Our work roots in Haddadin (2015); Schindlbeck and Haddadin (2015), where the basic UFIC regulation controller was proposed. In the present paper, we significantly advance this idea into a complete theoretical UFIC tracking framework, including rigorous stability analysis and extensive experimental evidence.

Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

Contact detection in a rib-reinforced vacuum driven actuator with an embedded CSR bridge sensor

ABSTRACT Stable control and accurate sensing are necessary for the safe operation of soft robots, and flexible sensors that can follow the flexible movements of soft robots are needed. In this paper, flexible bending sensors are embedded in a rib-reinforced vacuum-driven actuator with no risk of rupture, considering the application of flexible sensors to human cooperative work and wearable devices. The bending sensor is a bridge circuit with conductive silicone rubber (CSR) for variable resistance. The CSR bridge sensor consists of four CSRs that capture deformation and a circuit with a conductive coating spray printed on a laminated sheet. The sensor value is the output voltage divided by the input voltage, and the effects of the drift and input voltage variations are canceled out. The bending sensor embedded in the silicone actuator has a linear relationship, and the rib-reinforced vacuum driven actuator provides angle PID control for continuously changing reference inputs by feeding back sensor estimated angle. Furthermore, contact detection was established by using the pressure estimated from the CSR bridge sensor and the pressure sensor error as the threshold. The algorithm provides reliable contact detection and force estimation with actuator response thresholds and anti-chattering.

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation.

Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model's predictive capabilities and its applicability to practical scenarios. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench to replicate a knee subject to a total knee replacement. The original knee inserts of the training station were replaced with personalized 3D-printed bones incorporating their corresponding implants, and multiple sensors with their respective supports were added. The recorded movement of the patella was used in combination with the forces recorded by the pressure sensor and the load cells, to validate the results obtained from the simulation, which was performed by means of a multibody dynamics formulation implemented in a custom-developed library. The utilization of 3D-printed models and sensors facilitated cost-effective and replicable experimental validation of computational simulations, thereby advancing orthopedic surgery while circumventing ethical concerns.

Tactile sensor-less fingertip contact detection and force estimation for stable grasping with an under-actuated hand

Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks.

Variable Impedance Control for a Single Leg of a Quadruped Robot Based on Contact Force Estimation

Variable Impedance Control for a Single Leg of a Quadruped Robot Based on Contact Force Estimation

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Efficient implementation on accuracy improvement of the two-dimensional node-to-segment contact approach for explicit dynamic analysis

The penalty-method-based node-to-segment (NTS) approach is widely employed in the explicit dynamic analysis owing to its computational efficiency and implementation simplicity. However, the classical approach does not pass the contact patch test and results in severe inaccuracies. This study attempts the accuracy enhancement of an explicit dynamic contact analysis with minimum efficiency loss using the NTS algorithm with the modified area regularization technique (NTS-mAR). The computational procedure is compared to an allied modified penalty-method-based NTS approach, i.e., the virtual node-to-segment algorithm passing the patch test (VTS-PPT). Then, an extension to an explicit dynamic analysis framework is attempted, wherein the speed of the contact force calculation significantly influences the overall computational efficiency. The cost of the remaining computation was minimized by employing a lumped mass matrix and a one-point integration rule for the internal force. Elastoplasticity was considered to extend its application. The accuracy improvement compared to the classical one-pass NTS approach was similar for the modified approaches. The VTS-PPT approach requires more than twice the cost of contact force estimation compared with the classical one-pass NTS approach. In contrast, NTS-mAR approach induces a cost increase from 6 to 36% that of classical one-pass NTS approaches. For the given examples, the NTS-mAR approach is beneficial when an improvement in accuracy is desired with minimum efficiency loss.

Output-only estimation of lateral wheel-rail contact forces and track irregularities

The contact between the wheel and the rail heavily affects the dynamic behaviour of railway vehicles and their running safety. The real-time knowledge of wheel-rail contact forces is tricky due to the several running conditions of the train and the combined tribological and wear characteristics of wheels and rails. Furthermore, their measurement and that of track irregularities, sensitive to the harshness of the wheel-rail contact, require dedicated sensors. Therefore, the development of strategies for estimating wheel-rail contact features is essential to perform the condition monitoring of rails to improve the safety related to the entire railway system. A nonlinear model-based estimation procedure, based on a Central Difference Kalman Filter, is proposed in this work to estimate the lateral wheel-rail contact forces and moments, including the identification of lateral track irregularities in the estimation process related to rails. Furthermore, the developed estimation technique can be used as a tool to monitor continuously the track conditions through in-service railway vehicles, coupled with the possibility to improve the effectiveness of control systems onboard them by estimating wheel-rail contact interactions through a model-based estimator, which is fed by measurements obtained by the typical set of sensors adopted for condition monitoring purposes on railway vehicles.