External Contact Forces Research Articles

Analysis, simulation and experimental research on the mechanisms of lantern-shaped strand defects in the conductor construction of transmission line

This paper focuses on theoretical, numerical simulation, and experimental research on the lantern-shaped strand defect that arises in tension stringing constructions of large-section conductors. Starting from considerations for factors such as the construction environment and the characteristics of the conductor passing through blocks, this paper presents the causes of conductor torque generation and analyzes the distribution of axial forces on the outmost layer strands and the outmost adjacent layer strands of the conductor when subjected to bending and twisting. Taking into account the contact forces between strands in different layers, this paper thoroughly examines the mechanical causes that lead to the occurrence of the lantern-shaped strand defect in conductors. It concludes that the axial compression and the external contact force acting on the outermost adjacent layer strands are crucial in the formation of the lantern-shaped strand defect. To numerically simulate the lantern-shaped defect, a finite element model is developed to replicate the passage of a 1250 mm2 large-section conductor through the block system. Furthermore, considering actual working conditions, a test equipment is designed and constructed to replicate the lantern-shaped defect. The shape and size of the lantern-shaped defect obtained from the tests exhibited a substantial agreement with the simulation results, suggesting concordance between theoretical analysis and real situation. This research methodology can be used to analyze defects in conductors with varying sections, serving as a theoretical foundation for the prevention and control of construction defects in conductors.

A Passively Conforming Soft Robotic Gripper with Three-Dimensional Negative Bending Stiffness Fingers.

Robot grippers that lack physical compliance have a difficult time dealing with uncertainty, such as fragile objects that may not have well-defined shapes. Existing soft robotic grippers require a large empty workspace for their actuated fingers to curl around the objects of interest, limiting their performance in clutter. This article presents a three-dimensional structure that exhibits negative stiffness in every bending direction used as fingers in a class of soft robotic grippers. Our approach exploits a compliant mechanism in a conical shape such that a transverse external contact force causes the fingers to bend toward the contact, enabling passive conformation for an adaptive grasp, even in clutter. We show analytically and experimentally that the proposed fingers have a negative bending response and that they conform to objects of various diameters. We demonstrate a soft robotic gripper with three self-conforming fingers performing the following: (1) fingertip grasping, (2) power grasping, and (3) semipassive grasping in clutter. Grasping experiments focus on picking fruits, which exemplify delicate objects with unmodeled shapes with significant variation. The experimental results reveal the ability of the self-conforming structure to smoothly envelope a broad range of objects and demonstrate a 100% grasp success rate in the experiments performed. The proposed passively conforming fingers enable picking of complex and unknown geometries without disturbing nearby objects in clutter and without the need for complex grasping algorithms. The proposed structures can be tailored to deform in desired ways, enabling a robust strategy for the engineering of physical compliance for adaptive soft structures.

Kinematics and Stiffness Modeling of Soft Robot With a Concentric Backbone

Abstract Soft robots can undergo large elastic deformations and adapt to complex shapes. However, they lack the structural strength to withstand external loads due to the intrinsic compliance of fabrication materials (silicone or rubber). In this paper, we present a novel stiffness modulation approach that controls the robot’s stiffness on-demand without permanently affecting the intrinsic compliance of the elastomeric body. Inspired by concentric tube robots, this approach uses a Nitinol tube as the backbone, which can be slid in and out of the soft robot body to achieve robot pose or stiffness modulation. To validate the proposed idea, we fabricated a tendon-driven concentric tube (TDCT) soft robot and developed the model based on Cosserat rod theory. The model is validated in different scenarios by varying the joint-space tendon input and task-space external contact force. Experimental results indicate that the model is capable of estimating the shape of the TDCT soft robot with an average root-mean-square error (RMSE) of 0.90 (0.56% of total length) mm and average tip error of 1.49 (0.93% of total length) mm. Simulation studies demonstrate that the Nitinol backbone insertion can enhance the kinematic workspace and reduce the compliance of the TDCT soft robot by 57.7%. Two case studies (object manipulation and soft laparoscopic photodynamic therapy) are presented to demonstrate the potential application of the proposed design.

A Framework for Inclusion of Unmodelled Contact Tasks Dynamics in Industrial Robotics.

This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot.

Liquid Lens‐Based Optical Tactile Sensor with a Touch‐Sensing Separable Structure

AbstractOptical tactile sensing plays an important role in the tactile sensing field, due to its high sensitivity, fast response, and prominent interference immunity. Herein, based on the liquid lens, a low‐cost optical tactile sensor with a touch‐sensing separable structure is designed and fabricated, and it contains a touch unit and an optics sensing unit. The touch unit is a chamber structure with good integration, and the optics sensing unit is designed with a liquid‐membrane structure. Different from the existed optical tactile sensors, the principle of the proposed sensor is that the focal length of the optics sensing unit is changed with an external contact force, and then the directional light propagation direction produces change, which affects the perceived light intensity of the silicon photocell. From the sensing performance test results, this tactile sensor has good statics and dynamics performance, which embodies in the rapid response time (about 0.048 s), stable output, high sensitivity (the max sensitivity is about 7.1338 V/N), and resolution (0.01 N), low hysteresis ratio (less than 6.5%), and good linearity (R2 ≈ 0.98). In addition, some application experiments are conducted and the results demonstrate the application feasibility of the proposed sensor in health monitoring and multichannel sensing.

Active suspension control with consensus strategy for dynamic posture tracking of wheel-legged robotic systems on uneven surfaces

This work presents a dynamic posture tracking control strategy for wheel-legged systems on uneven surfaces. Based on the kinematic model of a wheel-legged robotic system, the expected positions for the end-effectors of wheel-legs are calculated according to posture references and sensor feedback. The position control problem for a general wheel-leg is investigated for the active mechanism to imitate a passive suspension and respond to the external contact forces. The position tracking accuracy of the wheel-leg is sacrificed to enhance the compliance performance under rough terrain. Because of the unique contact state with the uneven ground for each wheel-leg, the position responses are different. As a result, the forces from the wheel-legs to the fuselage are inconsistent, which leads to the risk of posture oscillations. Equipping the wheel-legs with an undirected communication network, a consensus scheme for the robotic system is developed with proven global asymptotic stability to improve the posture tracking property. A novel robotic system is established with Stewart-structured wheel-legs, which are connected by a user datagram protocol network. Comparative experimental results are carried out on the physical prototype to validate the effectiveness of the proposed approach.

Hybrid vision/force control for underwater vehicles landing on unknown surfaces

In this work, a hybrid vision/force controller is proposed for landing an underwater vehicle on a surface without exact surface attitude angles (e.g., dam or ship bottom) and maintaining the desired contact force. The visual impedance model is employed to adjust the reference HVS state vector according to the external force to ensure the dynamic performance during the landing process. The external contact force is estimated by a force observer in the absence of force sensors. The desired velocity is generated by a linear feedback control and modified by an optimization-based approach considering the camera constraints. In the dynamic velocity control, dynamic inversion with an adaptive neural network is used to overcome uncertain disturbances. Finally, numerical simulation results are presented to validate the effectiveness of the proposed landing strategy.

Investigation and fabrication triboelectric nanogenerator using commercial Polytetrafluoroethylene and Aluminum

Triboelectric nanogenerator (TENG) is an energy technology that can convert mechanical energy into electricity based on the conjunction of triboelectric friction and electrostatic induction. TENG possesses a high potential as an alternative artificial energy source to develop the integrated power source device, active sensor, or massive scale power source. In this research, the vertical contact triboelectric nanogenerators using commercial grade Polytetrafluoroethylene (PTFE) and Aluminum were successfully fabricated. It performs the output voltage and current of 145 V and 8.5 μA, respectively. Moreover, the maximum power of 510 μW was observed at the external contact force of 11 N.

Payload Identification and Gravity/Inertial Compensation for Six-Dimensional Force/Torque Sensor with a Fast and Robust Trajectory Design Approach.

In the robot contact operation, the robot relies on the multi-dimensional force/torque sensor installed at the end to sense the external contact force. When the effective load and speed of the robot are large, the gravity/inertial force generated by it will have a non-negligible impact on the output of the force sensor, which will seriously affect the accuracy and effect of the force control. The existing identification algorithm time is often longer, which also affects the efficiency of force control operations. In this paper, a self-developed multi-dimensional force sensor with integrated gravity/inertial force sensing function is used to directly measure the resultant force. Further, a method for the rapid identification of payload based on excitation trajectory is proposed. Firstly, both a gravity compensation algorithm and an inertial force compensation algorithm are introduced. Secondly, the optimal spatial recognition pose based on the excitation trajectory was designed, and the excitation trajectory of each joint is represented by a finite Fourier series. The least square method is used to calculate the identification parameters of the load, the gravity, and inertial force. Finally, the experiment was verified on the robot. The experimental results show that the algorithm can quickly identify the payload, and it is faster and more accurate than other algorithms.

Do non-rearfoot runners experience greater second metatarsal stresses than rearfoot runners?

Stress fracture of the second metatarsal is a common and problematic injury for runners. The choice of foot strike pattern is known to affect external kinetics and kinematics but its effect on internal loading of the metatarsals is not well understood. Models of various complexities can be used to investigate the effects of running characteristics on metatarsal stresses. This study aimed to compare second metatarsal stress between habitual rearfoot and non-rearfoot strikers during barefoot running, using a novel participant-specific finite element model, including accurate metatarsal and soft tissue geometry. Synchronised force and kinematic data were collected during barefoot overground running from 20 participants (12 rearfoot strikers). Stresses were calculated using a previously evaluated and published 3D finite element model. Non-rearfoot strikers demonstrated greater external loading and joint contact forces than rearfoot runners, but there were no differences in stresses between groups. Additionally, the study allowed for a qualitative assessment of bone geometries and stresses. No correlation was found between bone volume and stresses, however, there was found to be a large variation in metatarsal shapes, possibly accounting for the lack of difference in stresses. This emphasises the importance of bone geometry when estimating bone stress and supports the suggestion that external forces should not be assumed to be representative of internal loading.

Development of Admittance Control Method with Parameter Self-optimization for Hydraulic Series Elastic Actuator

Series elastic actuators (SEAs) have emerged as promising devices to enhance the safety of human-robot interactions in the manufacturing industry. However, the control of a hydraulic SEA under disturbance remains an unexplored issue. To address this problem, an admittance control method with parameter self-optimization is developed in this study. The hydraulic SEA and its dynamic model are first developed, and then, an admittance controller that combines a passive disturbance observer (DOB) and a feedback compensator is developed based on load movement dynamics. The control law of the framework is made independent of the hydraulic dynamics by considering the uncertainty and tracking error as disturbances. This simplifies the controller computation, enhances system robustness, and facilitates practical application. Next, the control performance is further improved by optimizing the control parameters using an improved crowding-based dynamic population size differential evolution (crowding-based dynNP-DE) algorithm. Benchmark and optimization experiments are performed to verify the superiority of the modified algorithm and obtain the control parameters. Finally, the optimized parameters are applied to practical experiments to validate the improved performance of the proposed admittance control scheme. The results show that the proposed method effectively reduces the SEA stiffness tracking error, with respect to the external contact force.

Force Reconstruction at Mechanical Interfaces Using a Modal Filtering Decomposition Approach

Traditional approaches to analyzing nonlinear systems often involve assuming a specific model form and model order for the nonlinearity. These nonlinearities in a system could instead be modeled as a nonlinear force applied to an underlying linear system. Force reconstruction methods can be used to recreate this nonlinear force from measured data, allowing the system change to be quantified. A modal based force reconstruction technique has proven capable of recreating force inputs at unmeasured locations and has been successfully applied to nonlinear intermittent external contact forces in previous studies. To extend these findings, a two-beam system was designed such that intermittent contact occurs near the joint locations, resulting in correlated forces. Previous work with this methodology has not addressed correlated loading conditions, so this work extends these techniques to the localization of correlated forces beyond measured points. The methodology is first demonstrated using analytical case studies, then performed experimentally. Acceleration responses are obtained from a sparse measurement grid which does not include the contact points, so the forces are reconstructed at unmeasured locations. Estimates of the nonlinear contact forces are calculated using the force reconstruction process, which are then compared to the measured force values. Cases are presented which address intermittent contact, loosening of preloaded connections, and the reconstruction of multiple connections simultaneously. Reconstructing correlated forces beyond measured points allows for the ability to locate and estimate forces without needing to assume the locations of these forces prior to acquiring data, extending the range of applications for these techniques.

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

The mechanical compliance of soft robots comes at a cost of higher uncertainty in their sensing and perception, which deteriorates the accuracy of predictive models. Predictive uncertainty, which expresses the confidence behind model predictions, is necessary to compensate for the loss of accuracy in soft robot perceptive models. Nevertheless, developing a general framework to capture uncertainties is further challenged by the complex dynamics of soft robots and the difficulties in sensorizing them. In this work, we present a predictive uncertainty estimation framework based on deep learning for soft robot multimodal sensing. We show that the framework can learn to quantify uncertainty and thus is able to express the confidence associated with the predictions during inference. Being data-driven, it is scalable to different types of soft robots and sensor modalities. We demonstrate the framework on a complex multimodal sensing task where a single flex sensor is used to predict the full-body configuration of a soft actuator, as well as the magnitude and location of external contact force. We also discuss how predictive uncertainties are critical to achieve safe learning and model interpretability in soft robotics.

Mechanics-Aware Modeling of Cloth Appearance.

Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a mechanically plausible way.

Estimating Tip Contact Forces for Concentric Tube Continuum Robots Based on Backbone Deflection

Concentric Tube Continuum Robots are among the smallest and most flexible instruments in development for minimally invasive surgery, thereby enabling operations in areas within the human body that are difficult to reach. Unfortunately, integrating state-of-the-art force sensors is challenging for these robots due to their small form factor, although contact forces are essential information in surgical procedures. In this work, we propose a novel data-driven approach based on Deep Direct Cascade Learning (DDCL) to create a virtual sensor for computing the tip contact force of Concentric Tube Continuum Robots. By exploiting the robot’s backbone’s inherent elasticity, deflection is used to estimate the respective external tip contact force. We evaluate our approach on different data representations for a single tube and apply it subsequently on a three-segment Concentric Tube Continuum Robot. Furthermore, we devise a novel transfer learning approach through DDCL to improve the estimation accuracy by pre-training a cascaded network with simulated data. Subsequently, we fine-tune the network based on a small real-world data set recorded from the physical robot.

How shape and flapping rate affect the distribution of fluid forces on flexible hydrofoils

Abstract

Modal-Based Kinematics and Contact Detection of Soft Robots.

Soft robots offer an alternative approach to manipulate within a constrained space while maintaining a safe interaction with the external environment. Owing to its adaptable compliance characteristic, external contact force can easily deform the robot shapes and lead to undesired robot kinematic and dynamic properties. Accurate contact detection and contact location estimation are of critical importance for soft robot modeling, control, trajectory planning, and eventually affect the success of task completion. In this article, we focus on the investigation of a one degree of freedom (1-DoF) soft pneumatic bending robot, which is regarded as one of the fundamental components to construct complex, multi-DoFs soft robots. This 1-DoF soft robot is modeled through the integral representation of the spatial curve, where direct and instantaneous kinematics are calculated explicitly through a modal method. The fixed centrode deviation method is used to detect the external contact and estimate the contact location. Simulation results and experimental studies indicate that the contact location can be accurately estimated by solving a nonlinear least-square optimization problem. Experimental validation shows that the proposed algorithm is able to successfully estimate the contact location with the estimation error of 1.46 mm.

Dynamic trajectory adjustment of lower limb exoskeleton in swing phase based on impedance control strategy

The lower limb exoskeleton provides assistance by following the lower limb joints’ desired motion trajectory. However, angle control is not enough to meet the requirements in some special circumstances such as encountering obstacles. In the swing phase of the attached leg with the exoskeleton, there is a different contact force between the sole and the road surface in different road conditions. Therefore, it is particularly important to control the joint angle and contact force simultaneously, that is, it is not only necessary to follow the desired angle but also to minimize the influence of external contact force. In this article, a novel scheme is proposed to adjust the trajectory dynamically in the swing phase. First of all, the physical model is streamlined and the Lagrangian principle is carried out to dynamic analysis and established a model of lower limb exoskeleton in the swing phase. Furthermore, the angle dynamics equation is transformed into a Cartesian coordinate system to calculate the end contact force for the impedance model. Finally, the impedance control strategy together with a disturbance observer is designed which is suitable for nonlinear and strong coupling characteristics. The simulation result shows that the control system can follow the angle accurately in the condition of minimizing external constraints. Hardware experiment shows that lower extremity exoskeleton can adjust motion trajectory actively when encountering obstacles and complete the movement trajectory tracking at the same time.

Further investigation on improved viscoelastic contact force model extended based on hertz's law in multibody system

• The shortcoming of the existing improved contact stiffness coefficients is analyzed systematically. • A new contact force model suited for the internal contact between the cylindrical bodies is proposed. • The other contact force model suited for the external contact between the cylindrical bodies is proposed. • The correctness of the external contact force model is validated by the bouncing example, the reasonability of the internal contact force model is proved by the experimental data. Since the Hertzian contact stiffness coefficient is closely related to the power exponent, the modification of the Hertzian contact stiffness coefficient leads to some problems: (i) the power exponent must be changed with the improved contact stiffness coefficient, otherwise, the unit of the contact force is not newton; (ii) since the power exponent is one of the factors to determine the magnitude of the contact force, the inappropriate power exponent makes the multibody system hard to convergence or lose physical meaning. The main goal of this investigation is to reveal the shortcoming of the improved contact stiffness coefficient, and develop two different contact force models suited for the internal and external contact form associated with the hysteresis damping factor from Lankarani-Nikravesh contact force model (L-N model). The effectiveness of the external contact force model is validated by the bouncing example. The correctness of the internal contact force model used in the multibody system with a clearance revolute joint is validated by the experimental data.

Camera-Based Optical-Fiber Tactile Sensor for Intraoperative Grasping Force Measurement

Introduction During minimally invasive surgery, surgeons insert specially-designed instruments through a small incision into the patient’s body. Despite all the advantages of this procedure, surgeons do not have the natural tactile force feedback in the surgery. Tactile feedback helps the surgeon to apply an appropriate force to avoid tissue damage [1]. Therefore, researchers have proposed different optical force sensors to attach at the tip of surgical instruments for measuring the tool-tissue interaction force [2]. To this end, a new sensing principle, i.e., variable bending radius (VBR) for light intensity modulation (LIM) in optical-fiber-based sensor was proposed and experimentally validated in our previous study [3]. This method allowed further miniaturization of MIS force sensors and increased its sensitivity. In the current study, a miniaturized VBR-based force sensor was fabricated using rapid prototyping techniques. The sensor was mounted on a custom-designed surgical grasper and an RGB camera was used to measure the transmitting light through the optical fiber. Finally, the calibrated sensor was studied for multiple grasping tasks on freshly excised bovine tissue. Material and Method As depicted in Fig. 1, the sensor has a flexible shell with a semi-circular indenter on the top of the optical fiber. Upon any contact between the flexible shell and tissue, the shell bends downward and the semi-circular indenter applies a force F on the optical fiber. Since optical fiber is anchored from both ends, it behaves as an Euler-Bernoulli beam under an external contact force. The sensor was 3D printed with an SLA printer with flexible and transparent resins. In order to acquire the output light intensity at the end of the optical fiber, an image-based intensity measurement system was developed. To this end, a Logitech C920 camera was used and a projection was 3D printed. The chamber was designed in such a way that the projection spot of laser in optical fiber would be visible in camera as depicted in Fig. 2(a). Noh et al. [4] have shown that average greyscale values, σ, of the image would be proportional to the total intensity of the laser spot and was defined as (1) where σi is the greyscale value of i-th pixel, and n = 640x480 was the total number of pixels in each frame. Fig. 2(b) depicts the variation of brightness of the laser spot as the sinusoidal force is applied on the sensor. Due to the bending of optical fiber, light intensity modulation happens as external force increases. In previous studies showed that the percentage of power decay in fiber is non-linearly dependent on the applied external force F. Also due to the viscoelastic properties of the 3D printed materials, the power decay is dependent on the rate of application of F and which would show its effects on the rate of change in output power. Therefore, a nonlinear rate-dependent learning-based support vector regression (SVR) calibration was trained with the data from a sinusoidal compression test (0-2N) with 0.5, 1.0, and 2.0Hz frequency. Fig. 2(c) depicts the training test setup. To further assess the performance of the proposed sensor in real surgical procedures, an ex-vivo test on freshly excised bovine muscle was performed (Fig. 3(a)). Also, to have a reference for the contact force between the jaws of the sensor and tissue, a FlexiForce sensor was calibrated and attached to the upper jaw of the sensorized grasper. Afterward, the tissue was manually grasped in the MIS grasper, while reference force was recorded through an Arduino MEGA2560 interface. In parallel, the force was estimated using the SVR formula. Results and Conclusions During the sinusoidal compression tests, the average greyscale value of the image taken by the camera decreased from 27- to 2-unit for 0 to 2N compressive force. SVR estimations showed an adjusted-R2 = 0.94 with a root-mean-square error of 0.09N between the testing machine measurements and SVR estimations. Also, SVR force estimations in the ex-vivo test showed fair agreement with the force readings from FlexiForce sensor (Fig. 3 (b)). In the ex-vivo validation test, the minimum detectable force of the sensor was 0.14N and the average error was 0.11±0.06N. As indicated in [1], force sensors for MIS applications must show a minimum detectable force of 0.2N with an average error of less than 0.2N. Therefore, authors conclude that the proposed and fabricated sensor with the implemented camera-based intensity measurement and the employed learning-based calibration technique have satisfactorily contributed to an applicable optical MIS force sensor. To expand the scope of this study, it is suggested that the ex-vivo tests be performed in an aqueous environment to study the effects of electrolytic interferences. Also, robustness of the proposed sensor against the electromagnetic interferences is to be studied.