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

Continuum manipulators enable minimally-invasive surgery on the beating heart, but the challenges involved in manually controlling the manipulator’s tip position and contact force with the tissue result in failed procedures and complications. The objective of this work is to achieve autonomous robotic control of a continuum manipulator’s position and force in a beating heart model. We present a model-less hybrid control approach that regulates the tip position/force of manipulators with unknown kinematics/mechanics, under unknown constraints along the manipulator’s body. The algorithms estimate the Jacobian in the presence of heartbeat disturbances and sensor noise in real time, enabling closed-loop control. Using this model-less control approach, a robotic catheter autonomously traced clinically relevant paths on a simulated beating heart environment while regulating contact force. A gating procedure is used to tighten the treatment margins and improve precision. Experimental results demonstrate the capabilities of the robot ([Formula: see text][Formula: see text]mm–[Formula: see text][Formula: see text]mm tracking error) while user demonstrations show the difficulty of manually performing the same task ([Formula: see text][Formula: see text]mm–[Formula: see text][Formula: see text]mm tracking error). This new, robotically-enabled contiguous ablation method could reduce ablation path discontinuities, improve consistency of treatment, and therefore improve clinical outcomes.

Analysis of deformation and multi-angle tip contact in a combined mechanism of thin-walled membrane forces and inextensible layers in soft actuators

An investigation on the relationship between grip, push and contact forces applied to a tool handle

In general, the contact force, which is used in controller design for robotic compliant motion, can be either explicitly calculated through complicated computation or measured by expensive force sensors. A force control strategy using the estimated contact force is proposed. The estimation of the contact force and states is primarily based on their previous observer design. It is shown that the controller achieves asymptotic stability.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

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.

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.

Current electrophysiology signal recording and mapping systems have limited dynamic range (DR) and bandwidth, which causes loss of valuable information during acquisition of cardiac signals. We evaluated a novel advanced signal processing platform with the objective to obtain and assess additional information of clinical importance. Over 10 canines, we compared intracardiac recordings within all cardiac chambers, in various rhythms, in pacing and during radiofrequency (RF) ablation across two platforms; a conventional system and the PURE EP™ [(PEP); Bio Sig Technologies, Inc., Los Angeles, CA, USA]. Recording cardiac signals with varying amplitudes were consistently and reproducibly observed, without loss of detail or introduction of artefact. Further the amplitude of current of injury (COI) on the unipolar signals correlated with the instantaneous contact force (CF) recorded on the sensing catheter in all the animals (r2 = 0.94 in ventricle). The maximum change in the unipolar COI correlated with the change in local electrogram amplitude during non-irrigated RF ablation (r2 = 0.61 in atrium). Reduction in artefact attributable to pacing (20 sites) and noise during ablation (48 sites) was present on the PEP system. Within the PEP system, simultaneous display of identical signals, filtered differently, aided the visualization of discrete conduction tissue signals. Compared to current system, the PEP system provided incremental information including identifying conduction tissue signals, estimates of CF and a surrogate for lesion formation. This novel signal processing platform with increased DR and minimal front-end filtering may be useful in clinical practice.

Cable-driven continuum robots are widely used for endoluminal intervention because of their dexterity and shape conforming steerability. However, body contact between the continuum robot and its surrounding anatomy is unavoidable, which imposes a potential safety risk, including vessel wall damage or even perforation. This paper presents an approach for body contact estimation of continuum robots with tension-profile sensing of actuation fibers. First, tension-sensing optical fibers with multiple inscribed fiber Bragg grating (FBG) sensors are used for both actuation and in-situ sensing of the continuum robot. Second, a beam theory-based mechanical model considering segmental differences, multiple fiber interactions and external force interactions is established, followed by robust estimation of contact positions and forces. Finally, detailed simulations are conducted to validate the accuracy and effectiveness of the proposed method. Experiments on a notched continuum robot are carried out, and the results show that the proposed approach can effectively recover in-situ segmental actuation forces without the need of explicit modeling of the friction between the fibers and guiding channels. The method enables the estimation of the number of contact points, as well as contact positions and contact forces along the body of the continuum robot.

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.

In this paper we extend our previous work concerning the regulation of the contact force in active train pantographs with wire-actuators. In particular, we consider the problem of estimating the contact force, instead of measuring it, by means of the measured displacements of the upper and lower pantograph frames. The estimation method, based on the algebraic observability theory, entails the use of real-time sliding-mode differentiators. It makes use explicitly of the upper frame mechanical characteristics, but does not require any other information concerning the system model. We consider our previously proposed second-order sliding mode control scheme, which also includes proper linear compensating cascade filters devoted to cope with the de-stabilizing effects of the resonant wire actuator. We show that such a control scheme can be effectively combined with the given contact force estimation method.

Continuum robots enable safe and adaptive interaction with complex, unstructured, or constrained environments through continuous deformation, making them particularly suitable for medical and industrial applications. Accurate contact force sensing is essential to ensure safe and effective physical interaction in such scenarios. Although various embedded force sensors have been developed, sensor‐free approaches offer advantages in miniaturization, cost‐effectiveness, and biocompatibility. This review provides a comprehensive overview of sensor‐free contact force estimation methods for continuum robots, with an emphasis on algorithmic principles rather than specific continuum robot designs or applications. First, contact forces reported in the literature are systematically classified according to their distribution, components, and dynamics. Next, existing force estimation methods are divided into three categories: actuation‐based, deflection‐based, and environment‐based. For each category, the underlying algorithmic principles are discussed, representative challenges are highlighted, and their typical application scenarios are outlined. Finally, emerging trends and potential directions for future research are outlined.

A floating base system is inevitably to contact the environment while it is moving. This paper explores the contact force estimation and regulation algorithm for a position-controlled floating base system without joint torque information. First, the joint space dynamic model of the system is presented and transformed into the contact space. Then, the inverse dynamics method is employed to estimate the contact forces. After that, a proportional-integral (PI) regulator is designed to drive the contact forces to track the desired values. Finally, the feasibility of this algorithm is demonstrated on a simulated bipedal platform.

Space robots have been proposed to perform several tasks in orbit, such as repairing, refueling, and assembly. All those activities require the robot to interact with external objects in a safe and compliant form to avoid high impact forces that may damage the robot's components or the object to manipulate. Impedance controllers have demonstrated to be a suitable approach to perform similar tasks in ground. Therefore, implementing and analyzing impedance-based techniques in space is also worthy of study. Not only that, but also, methods that can reduce the number of components, weight and complexity should be considered. This article proposes the use of the disturbance observer for the design of an impedance controller to perform a safe capture of a target satellite by a space robot. First, the observer aids in the determination of the contact force between the servicer and the target. Later, once the almost perfect match between the actual and estimated contact force is demonstrated, the estimated force is used as input for an impedance controller with trajectory tracking. The advantages of estimating the contact force instead of measuring it are also discussed in the document. The design of the impedance control is performed such that the capturing process is compliant and safe, and, thus, it can help to advance toward an autonomous capture of space objects. A simulation experiment was set up to evaluate the performance of the proposed method. The results demonstrate that the contact force is bounded and behaves smoothly in a spring-mass-damper-like manner, imposing a stable contact between the robot's end effector and the servicer's capturing spot.

ABSTRACTRelative indentation is the input signal estimating contact force model parameters, so the signal is required to have a higher precision to ensure the accuracy of the estimated contact force model parameters. However, in the impact experiment, the vibration displacements in multiple directions are often coupled in the relative indentation, resulting in a large error of the measured relative indentation. This paper presents an experimental correction method for the relative indentation. Firstly, the relative indentation is decoupled by the established model of the spatial position of the hammerhead relative to the sample to reduce the errors caused by the rotation of the pendulum boom and the vibration of the base. A pendulum impact test device is established to verify the correction method of relative indentation. The results show that the maximum relative error between the contact force estimated by using the corrected relative indentation as the input signal and the measured contact force is less than 3%. The estimated contact force is in good agreement with the measured value, and the correlation coefficient is above 0.92. It shows that the experimental correction of the relative indentation has achieved good results, which verifies the accuracy of the correction method.