Journal articles: 'Joint torques estimation'

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Yousefizadeh, Shirin, and Thomas Bak. "Unknown External Force Estimation and Collision Detection for a Cooperative Robot." Robotica 38, no. 9 (December 20, 2019): 1665–81. http://dx.doi.org/10.1017/s0263574719001681.

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SUMMARYIn human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions.

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Imamura, Yumeko, Ko Ayusawa, Eiichi Yoshida, and Takayuki Tanaka. "Evaluation Framework for Passive Assistive Device Based on Humanoid Experiments." International Journal of Humanoid Robotics 15, no. 03 (June 2018): 1750026. http://dx.doi.org/10.1142/s0219843617500268.

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This study presents an enhanced framework for evaluating an assistive effect generated by a passive assistive device using a humanoid robot. The humanoid robotic experiments can evaluate wearable devices by measuring the joint torque, which cannot be measured directly from the human body. In this paper, we introduce an “assistive torque estimation map” as an efficient means for estimating the supportive torque within the range of motions by interpolating the measured joint torques and joint angles of the robot. This map aims to estimate the supportive torques for complex motions without conducting humanoid experiments or human-subject experiments with these motions. We generated an estimation map for an actual assistive suit that decreases the load on the lumbar region and we verified the validity of the proposed method by experimentation. In addition, the geometric simulation model of the assistive suit was validated based on the proposed experiments by using the humanoid robot HRP-4. The proposed framework is expected to lead to an efficient design of such assistive devices so that fewer human-subject experiments need to be conducted.

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Lam, Shui Kan, and Ivan Vujaklija. "Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study." Sensors 21, no. 19 (October 2, 2021): 6597. http://dx.doi.org/10.3390/s21196597.

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Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

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Kuo, A. D. "A Least-Squares Estimation Approach to Improving the Precision of Inverse Dynamics Computations." Journal of Biomechanical Engineering 120, no. 1 (February 1, 1998): 148–59. http://dx.doi.org/10.1115/1.2834295.

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A least-squares approach to computing inverse dynamics is proposed. The method utilizes equations of motion for a multi-segment body, incorporating terms for ground reaction forces and torques. The resulting system is overdetermined at each point in time, because kinematic and force measurements outnumber unknown torques, and may be solved using weighted least squares to yield estimates of the joint torques and joint angular accelerations that best match measured data. An error analysis makes it possible to predict error magnitudes for both conventional and least-squares methods. A modification of the method also makes it possible to reject constant biases such as those arising from misalignment of force plate and kinematic measurement reference frames. A benchmark case is presented, which demonstrates reductions in joint torque errors on the order of 30 percent compared to the conventional Newton–Euler method, for a wide range of noise levels on measured data. The advantages over the Newton–Euler method include making best use of all available measurements, ability to function when less than a full complement of ground reaction forces is measured, suppression of residual torques acting on the top-most body segment, and the rejection of constant biases in data.

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Latella, Claudia, Silvio Traversaro, Diego Ferigo, Yeshasvi Tirupachuri, Lorenzo Rapetti, Francisco Javier Andrade Chavez, Francesco Nori, and Daniele Pucci. "Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques." Sensors 19, no. 12 (June 21, 2019): 2794. http://dx.doi.org/10.3390/s19122794.

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The paper presents a stochastic methodology for the simultaneous floating-base estimation of the human whole-body kinematics and dynamics (i.e., joint torques, internal and external forces). The paper builds upon our former work where a fixed-base formulation had been developed for the human estimation problem. The presented approach is validated by presenting experimental results of a health subject equipped with a wearable motion tracking system and a pair of shoes sensorized with force/torque sensors while performing different motion tasks, e.g., walking on a treadmill. The results show that joint torque estimates obtained by using floating-base and fixed-base approaches match satisfactorily, thus validating the present approach.

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Petró, Bálint, and Rita M. Kiss. "Validation of the Estimated Torques of an Open-chain Kinematic Model of the Human Body." Periodica Polytechnica Mechanical Engineering 66, no. 2 (March 22, 2022): 175–82. http://dx.doi.org/10.3311/ppme.19920.

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The standing human body is frequently modeled as an inverted double pendulum restricted to a single plane. In order to capture the coordination efforts and interplay between spatial dimensions, the model has to capture motion and joint torques in all spatial dimensions. Our two-segment model covers two degrees of freedom (ML and AP revolutions) at the ankle and the hip level and utilizes the Denavit-Hartenberg convention. This work aimed to validate the model's torque estimation on a diverse group of participants (11 women, 22–56 years, 11 men, 22–61 years). The inverse dynamic calculations provide estimated joint torques for a motion capture recorded trial, while standing on a force platform enables the indirect measurement of ankle torques. A 60-second-long visually guided balancing task was recorded and repeated three times. The estimated and the indirectly measured torques were compared, and offset and variance type errors ( normalized RMSE and R2 ) were analyzed. The R2-values were excellent (R2 > 0.90) 64 out of the 66 cases (97%) for AP torques and 58 out of the 66 cases (88%) for ML torques. Normalized RMSE values were dominantly under the 0.35 value with some outliers. RMSE showed no evident connection with age, body height, body mass, or BMI. An open-chain kinematic model with two segments, following the Denavit-Hartenberg convention, is well suited to estimate the control torque traces of the human body during standing balancing and needs only three tracked positions.

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Ajayi, Michael Oluwatosin, Karim Djouani, and Yskandar Hamam. "Bounded Control of an Actuated Lower-Limb Exoskeleton." Journal of Robotics 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/2423643.

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A bounded control strategy is employed for the rehabilitation and assistance of a patient with lower-limb disorder. Complete and partial lower-limb motor function disorders are considered. This application is centered on the knee and the ankle joint level, thereby considering a user in a sitting position. A high gain observer is used in the estimation of the angular position and angular velocities which is then applied to the estimation of the joint torques. The level of human contribution is feedback of a fraction of the estimated joint torque. This is utilised in order to meet the demands for a bounded human torque; that is, τh≤N2,n≤N1,n. The asymptotic stability of the bounded control law without human contribution and the convergence analysis of the high gain observer is verified using Lyapunov-based analysis. Simulations are performed to verify the proposed control law. Results obtained guarantee a fair trajectory tracking of the physiotherapist trajectory.

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Haraguchi, Naoto, and Kazunori Hase. "Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques." Sensors 24, no. 9 (April 27, 2024): 2792. http://dx.doi.org/10.3390/s24092792.

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The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.

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Muhammad Isa, Munawwarah Solihah, Nurhidayah Omar, Mohammad Shahril Salim, Saidatul Ardeenawatie Awang, and Suhizaz Sudin. "Dynamic Modelling of the Spine for the Estimation of Vertebral Joint Torques using Gordon’s Method." Journal of Advanced Research in Applied Mechanics 125, no. 1 (October 2, 2024): 42–57. http://dx.doi.org/10.37934/aram.125.1.4257.

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World Health Organization (WHO) recognised musculoskeletal disorder (MSD) as the main contributor to disability worldwide, with low back pain as the major disorder globally. The occupational disorder normally occurs during lifting. The weight of the load and manual handling tasks during lifting has an impact on the spine and joint torque. The purpose of this study is to propose a dynamic model of the spine that can estimate the vertebral joint torques. This study is a bimodal approach that consists of the experimental and theoretical parts. Ten healthy UniMAP students (10 males) participated in this study. The subjects were required to lift a 3kg weight plate for kinematics and EMG data collection. Retro-reflective markers were attached to the subject body, and then, the data was collected and stored in QTM software. Kinematic data was processed using C-Motion Visual3D. Eight Trigno Wireless Sensors were attached on the back muscles (left and right erector spinae, latissimus dorsi, external oblique and internal oblique). The EMG data were stored in EMG Acquisition software and subsequently, were processed using EMG Analysis software. Gordon’s method was used to develop a mathematical model of the spine. The model comprises of five kinematic chains which connected three lumbar, two thoracic and one cervical. The model calculated the value of joint torque on flexion/extension movement using Matlab and Microsoft Excel. When calculated on L5, the model gives an estimation within 0 – 30 kgm2s-2. The model was further used to estimate value of L3, L1, MAI and T2. The estimate average value of joint torque at L3 is within 5 – 25 kgm2s-2, MAI is within 0 – 6 kgm2s-2 and T2 is within 0 – 1 kgm2s-2. The average RMS values show the highest muscle activity on the right internal oblique muscle (1519 µV), followed by the right external oblique (1166 µV) and left external oblique (418 µV). The results obtained gives an insight on the value of joint torque that have been applied by the spine and the most activated back muscles during lifting.

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Lim, T. G., H. S. Cho, and W. K. Chung. "A parameter identification method for robot dynamic models using a balancing mechanism." Robotica 7, no. 4 (October 1989): 327–37. http://dx.doi.org/10.1017/s026357470000672x.

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SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.

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Abdullahi, Auwalu Muhammad, and Ronnapee Chaichaowarat. "Sensorless Estimation of Human Joint Torque for Robust Tracking Control of Lower-Limb Exoskeleton Assistive Gait Rehabilitation." Journal of Sensor and Actuator Networks 12, no. 4 (July 7, 2023): 53. http://dx.doi.org/10.3390/jsan12040053.

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Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training.

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Kružić, Stanko, Josip Musić, Vladan Papić, and Roman Kamnik. "Strain Gauge Neural Network-Based Estimation as an Alternative for Force and Torque Sensor Measurements in Robot Manipulators." Applied Sciences 13, no. 18 (September 11, 2023): 10217. http://dx.doi.org/10.3390/app131810217.

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When a robotic manipulator interacts with its environment, the end-effector forces need to be measured to assess if a task has been completed successfully and for safety reasons. Traditionally, these forces are either measured directly by a 6-dimensional (6D) force–torque sensor (mounted on a robot’s wrist) or by estimation methods based on observers, which require knowledge of the robot’s exact model. Contrary to this, the proposed approach is based on using an array of low-cost 1-dimensional (1D) strain gauge sensors mounted beneath the robot’s base in conjunction with time series neural networks, to estimate both the end-effector 3-dimensional (3D) interaction forces as well as robot joint torques. The method does not require knowledge of robot dynamics. For comparison reasons, the same approach was used but with 6D force sensor measurements mounted beneath the robot’s base. The trained networks showed reasonably good performance, using the long-short term memory (LSTM) architecture, with a root mean squared error (RMSE) of 1.945 N (vs. 2.004 N; 6D force–torque sensor-based) for end-effector force estimation and 3.006 Nm (vs. 3.043 Nm; 6D force–torque sensor-based) for robot joint torque estimation. The obtained results for an array of 1D strain gauges were comparable with those obtained with a robot’s built-in sensor, demonstrating the validity of the proposed approach.

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Abdullahi, Auwalu Muhammad, Ado Haruna, and Ronnapee Chaichaowarat. "Hybrid Adaptive Impedance and Admittance Control Based on the Sensorless Estimation of Interaction Joint Torque for Exoskeletons: A Case Study of an Upper Limb Rehabilitation Robot." Journal of Sensor and Actuator Networks 13, no. 2 (March 28, 2024): 24. http://dx.doi.org/10.3390/jsan13020024.

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Physiotherapy is the treatment to recover a patient’s mobility and limb function after an injury, illness, or disability. Rehabilitation robots can be used to replace human physiotherapists. To ensure safety during robot physical therapy, the patient’s limb needs to be controlled to track a desired joint trajectory, and the torque due to interaction force/torque needs to be measured and regulated. Therefore, hybrid impedance and admittance with position control (HIPC) is required to track the trajectory and simultaneously regulate the contact torque. The literature describes two structures of HIPC: (1) a switched framework between admittance and impedance control operating in parallel (HIPCSW); and (2) a series connection between admittance and impedance control without switching. In this study, a hybrid adaptive impedance and position-based admittance control (HAIPC) in series is developed, which consists of a proportional derivative-based admittance position controller with gravitational torque compensation and an adaptive impedance controller. An extended state observer is used to estimate the interaction joint torque due to human stiff contact with the exoskeleton without the use of force/torque sensor, which is then used in the adaptive algorithm to update the stiffness and damping gains of the adaptive impedance controller. Simulation results obtained using MATLAB show that the proposed HAIPC significantly reduces the mean absolute values of the actuation torques (control inputs) required for the shoulder and elbow joints in comparison with HIPC and HIPCSW.

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Štrbac, Matija, and Dejan B. Popović. "Software Tool for the Prosthetic Foot Modeling and Stiffness Optimization." Computational and Mathematical Methods in Medicine 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/421796.

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We present the procedure for the optimization of the stiffness of the prosthetic foot. The procedure allows the selection of the elements of the foot and the materials used for the design. The procedure is based on the optimization where the cost function is the minimization of the difference between the knee joint torques of healthy walking and the walking with the transfemural prosthesis. We present a simulation environment that allows the user to interactively vary the foot geometry and track the changes in the knee torque that arise from these adjustments. The software allows the estimation of the optimal prosthetic foot elasticity and geometry. We show that altering model attributes such as the length of the elastic foot segment or its elasticity leads to significant changes in the estimated knee torque required for a given trajectory.

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Tadikonda, S., and H. Baruh. "Pointwise-Optimal Control of Robotic Manipulators." Journal of Dynamic Systems, Measurement, and Control 110, no. 2 (June 1, 1988): 210–13. http://dx.doi.org/10.1115/1.3152673.

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A method is presented for the pointwise-optimal control of robotic manipulators along a desired trajectory. An approximate expression for the manipulator response is used to minimize a quadratic performance index with a linear regulator and tracking criterion, during each sampling period. The delay associated with implementation of the control action is analyzed, and its adverse effects are eliminated by estimation of the joint angles and torques one time step ahead.

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GÜNTHER, M., H. WITTE, and R. BLICKHAN. "JOINT ENERGY BALANCES: THE COMMITMENT TO THE SYNCHRONIZATION OF MEASURING SYSTEMS." Journal of Mechanics in Medicine and Biology 05, no. 01 (March 2005): 139–49. http://dx.doi.org/10.1142/s0219519405001345.

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Our study quantifies the amount of error induced in the calculated energy balances of the joints if a trigger offset between the measurement of ground reaction force and of video data occurs. Joint energy balances constitute the basis for an adequate interpretation of muscular activity. An estimation of the amount of this error introduced by deficient synchronization has not been published so far but currently seems to be essential in the face of commercial providers offering complete solutions from data acquisition up to inverse dynamics analyses. As an example, we applied an inverse dynamics process to a data set of the contact phase of human running where the synchronization was disturbed artificially. We compared the amount of error for different methods of inverse dynamics. We found that a time offset of 5 ms results in almost 100% error (compared to zero offset) in the energy balance of each joint (up to 28 J in the hip). A kinematic event appearing later on the time scale than the respective kinetics shifts the calculated main source of energy production from the ankle to the hip, and vice versa if appearing precipitate. This 5 ms synchronization error is even higher than the methodical error introduced when synchronizing correctly but using the static torque equilibrium instead of complex inverse dynamics for the calculation of joint torques. We conclude that when buying professional analysis systems a strong urge to prove exact synchronization should be put on the provider.

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Luh, Jer-Junn, Gwo-Ching Chang, Cheng-Kung Cheng, Jin-Shin Lai, and Te-Son Kuo. "Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model." Journal of Electromyography and Kinesiology 9, no. 3 (April 1999): 173–83. http://dx.doi.org/10.1016/s1050-6411(98)00030-3.

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Dai, Chenyun, Stephane Martel, Francois Martel, Denis Rancourt, and Edward A. Clancy. "Single-trial estimation of quasi-static EMG-to-joint-mechanical-impedance relationship over a range of joint torques." Journal of Electromyography and Kinesiology 45 (April 2019): 18–25. http://dx.doi.org/10.1016/j.jelekin.2019.02.001.

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Zhang, Jiyu, Wei Gao, and Qing Guo. "Extended State Observer-Based Sliding Mode Control Design of Two-DOF Lower Limb Exoskeleton." Actuators 12, no. 11 (October 27, 2023): 402. http://dx.doi.org/10.3390/act12110402.

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Due to some model uncertainties and unknown friction disturbances that exist in the 2-DOF lower limb exoskeleton, a linear extended state observer (LESO) is proposed to estimate the unmeasurable angular velocity of two joints and the lumped uncertainties caused by friction disturbance and hydraulic parametric uncertainties. Meanwhile, by using the Lyapunov technique, a sliding mode controller is designed to improve the dynamic performance and the steady state accuracy of two joint angle responses in human–exoskeleton cooperative motion. By regulating the sliding mode controller gain, both the system state errors and estimation errors of the LESO are reduced in an arbitrary boundary of zero neighborhood. Finally, the effectiveness of the proposed control scheme is verified with both simulation and experimental results for one operator-wearable test, to guarantee that the joint position tracking performance and human–exoskeleton impedance torques are suppressed in a satisfactory boundary.

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Gaudin, H., and G. Bessonnet. "From identification to motion optimization of a planar manipulator." Robotica 13, no. 2 (March 1995): 123–32. http://dx.doi.org/10.1017/s0263574700017628.

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SummaryIdentification of inertia constants and joint frictions of a robot manipulator is achieved in situ, without dismantling operations, by means of specific test motions. The necessary estimation of actuating torques is carried out by measuring, with Hall effect transducers, the current absorbed by the motors which power the system. This identification is accomplished by using a precise methodological order adapted to a planar SCARA type manipulator with two degrees of freedom. The identification of friction laws underscores a hysteresis phenomenon of the dissipative torques. This indicates that friction doesn't result from a simple superposition of a dry friction law and a viscous damping law. The identification results were applied with success to implementation of optimized trajectories computed on the basis of a dynamic criterion. The effective minimization of the performance criterion along the optimized trajectories, according to the corresponding standard trajectories, was verified experimentally by evaluating the motor work and actuator torques.

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GUELTON, Kevin, Sébastien DELPRAT, and Thierry Marie GUERRA. "JOINT TORQUES ESTIMATION IN HUMAN STANDING BASED ON A FUZZY DESCRIPTOR UNKNOWN INPUTS OBSERVER." IFAC Proceedings Volumes 39, no. 18 (2006): 405–10. http://dx.doi.org/10.3182/20060920-3-fr-2912.00073.

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Ziai, Amirreza, and Carlo Menon. "Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography." Journal of NeuroEngineering and Rehabilitation 8, no. 1 (2011): 56. http://dx.doi.org/10.1186/1743-0003-8-56.

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Cheng, Hongtai, and Hongfei Jiang. "Sensorless force estimation and control of Delta robot with limited access interface." Industrial Robot: An International Journal 45, no. 5 (August 20, 2018): 611–22. http://dx.doi.org/10.1108/ir-03-2018-0048.

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Purpose Delta robot is a parallel robot specifically designed for high-speed pick and place tasks. However, sometimes they are asked to perform additional assembling and squeezing actions, which is beyond the capability of position-controlled Delta robots. Force sensors may be expensive and add mass to the system. Therefore, the purpose of this paper is to study sensorless force control of Delta robots using limited access interface. Design/methodology/approach Static force analysis is performed to establish a relation between joint torques and external forces. The joint torques are observed from signals provided by motor drivers. A distributed mass model is proposed to compensate the gravity of upper arms and forearms. To minimize the effect of backlash and nonlinear frictions brought by gearboxes, model parameters are calibrated in two separated modes: “LIFTING” and “LOWERING”. Finally, a hybrid force estimation model is built to deal with both cases simultaneously. Surrogate model-based force control law is proposed to increase the force control loop rate and handle the force control problem for discrete position-controlled Delta robots. Findings The results show that the force estimation model is effective and mode separation can significantly improve the accuracy. The force control laws indeed stabilize the robot in desired states. Originality/value The proposed solution is based on position-controlled commercial Delta robot and requires no additional force sensor. It is able to extend Delta robots’ capability and meet requirements of emerging complex tasks.

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Racz, Sever-Gabriel, Mihai Crenganiș, Radu-Eugen Breaz, Alexandru Bârsan, Claudia-Emilia Gîrjob, Cristina-Maria Biriș, and Melania Tera. "Integrating Trajectory Planning with Kinematic Analysis and Joint Torques Estimation for an Industrial Robot Used in Incremental Forming Operations." Machines 10, no. 7 (June 30, 2022): 531. http://dx.doi.org/10.3390/machines10070531.

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Robot manufacturing involves continuous path control, which is now available for both robotic controllers and CAM software packages. However, CAM solutions are focused on generating the code for the robotic structure to follow the toolpath, without taking into consideration the dynamics and energy consumption. In this study, robot incremental forming was considered as the manufacturing process, and a simulation model, based upon Matlab-Simulink Simscape Multibody technology, was developed. The proposed model was fed with the trajectory information generated by the CAM program, and using an inverse kinematics function, it was able to generate the commands to drive the robotic structure on the technological toolpaths. The model was also used to study the dynamic behavior of the robot; external experimental data from a 3D force sensor were fed to the model to include the influence of the technological forces which appeared during the incremental forming process. Thus, using the proposed model in conjunction with the external CAM software, the influence of the workpiece position upon the joint torques could be estimated, opening the way for future optimization. The shortcomings of the model, mainly involving inaccurate information with regard to the physical properties of the robotic structure, were addressed by subtracting the dry-run joint torques from those obtained from the technological process.

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Pontaga, I. "Knee joint stability estimation by hamstrings/quadriceps femoris muscles torques ratios in range of movements." Journal of Biomechanics 39 (January 2006): S73. http://dx.doi.org/10.1016/s0021-9290(06)83180-5.

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Liu, Zhiguang, Fei Yu, Liang Zhang, and Tiejun Li. "Real-Time Estimation of Sensorless Planar Robot Contact Information." Journal of Robotics and Mechatronics 29, no. 3 (June 20, 2017): 557–65. http://dx.doi.org/10.20965/jrm.2017.p0557.

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[abstFig src='/00290003/11.jpg' width='300' text='Estimation of robot contact information' ] The real-time estimation of sensorless planar robot contact information is a very important but also difficult subject in human-robot interaction. This paper proposes a method for the real-time estimation of contact location and contact force along a planar joint robot manipulator without using external sensory systems. A momentum-based method is used to estimate external joint torques due to the contact force and to determine a minimum contact range firstly. A nonlinear constrained optimization algorithm is presented to search the contact point. The contact force is calculated by dynamics. The searching space determined by the momentum-based approach is limited within the length range of the contact arm, so the solution speed of the optimization algorithm is high. The proposed method of combining observation algorithm and optimization algorithm transforms a complex detection problem of the any contact point on the robot body into a simple one-dimensional optimization solution with simple bound. The effectiveness of the proposed approach is validated through simulations and experimental results for the planar robot manipulator.

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Zou, Shuizhong, Bo Pan, Yili Fu, and Shuixiang Guo. "Improving backdrivability in preoperative manual manipulability of minimally invasive surgery robot." Industrial Robot: An International Journal 45, no. 1 (January 15, 2018): 127–40. http://dx.doi.org/10.1108/ir-02-2017-0031.

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Purpose The purpose of this paper is to propose a control algorithm to improve the backdrivability performance of minimally invasive surgical robotic arms, so that precise manual manipulations of robotic arms can be performed in the preoperative operation. Design/methodology/approach First, the flexible-joint dynamic model of the 3-degree of freedom remote center motion (RCM) mechanisms of minimally invasive surgery (MIS) robot is derived and its dynamic parameters and friction parameters are identified. Next, the angular velocities and angular accelerations of joints are estimated in real time by the designed Kalman filter. Finally, a control algorithm based on Kalman filter is proposed to enhance the backdrivability of RCM mechanisms by compensating for the internally generated gravitational, frictional and inertial resistances experienced during the positioning and orientating. Findings The parameter identification for RCM mechanisms can be experimentally evaluated from comparison between the measured torques and the reconstructed torques. The accuracy and convergence of the real-time estimation of angular velocity and acceleration of the joint by the designed Kalman filter can be verified from corresponding simulation experiments. Manual adjustment experiments and animal experiments validate the effectiveness of the proposed backdrivability control algorithm. Research limitations/implications The backdrivability control algorithm presented in this paper is a universal method to enhance the manual operation performance of robots, which can be used not only in the medical robot preoperative manual manipulation but also in robot haptic interaction, industrial robot direct teaching and active rehabilitation training of rehabilitation robot and so on. Originality/value Compared with other backdrivability design methods, the proposed algorithm achieves good backdrivability for RCM mechanisms without using force sensors and accelerometers. In addition, this paper presents a new static friction compensation approach for a joint moving with very low velocity.

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Roy, Shibendu Shekhar, Ajay Kumar Singh, and Dilip Kumar Pratihar. "Estimation of optimal feet forces and joint torques for on-line control of six-legged robot." Robotics and Computer-Integrated Manufacturing 27, no. 5 (October 2011): 910–17. http://dx.doi.org/10.1016/j.rcim.2011.03.002.

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Li, Mantian, Jing Deng, Fusheng Zha, Shiyin Qiu, Xin Wang, and Fei Chen. "Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot." Applied Sciences 8, no. 9 (September 11, 2018): 1610. http://dx.doi.org/10.3390/app8091610.

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Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.

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Engelhart, D., J. H. Pasma, A. C. Schouten, R. G. K. M. Aarts, C. G. M. Meskers, A. B. Maier, and H. van der Kooij. "Adaptation of multijoint coordination during standing balance in healthy young and healthy old individuals." Journal of Neurophysiology 115, no. 3 (March 1, 2016): 1422–35. http://dx.doi.org/10.1152/jn.00030.2015.

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Standing balance requires multijoint coordination between the ankles and hips. We investigated how humans adapt their multijoint coordination to adjust to various conditions and whether the adaptation differed between healthy young participants and healthy elderly. Balance was disturbed by push/pull rods, applying two continuous and independent force disturbances at the level of the hip and between the shoulder blades. In addition, external force fields were applied, represented by an external stiffness at the hip, either stabilizing or destabilizing the participants' balance. Multivariate closed-loop system-identification techniques were used to describe the neuromuscular control mechanisms by quantifying the corrective joint torques as a response to body sway, represented by frequency response functions (FRFs). Model fits on the FRFs resulted in an estimation of time delays, intrinsic stiffness, reflexive stiffness, and reflexive damping of both the ankle and hip joint. The elderly generated similar corrective joint torques but had reduced body sway compared with the young participants, corresponding to the increased FRF magnitude with age. When a stabilizing or destabilizing external force field was applied at the hip, both young and elderly participants adapted their multijoint coordination by lowering or respectively increasing their neuromuscular control actions around the ankles, expressed in a change of FRF magnitude. However, the elderly adapted less compared with the young participants. Model fits on the FRFs showed that elderly had higher intrinsic and reflexive stiffness of the ankle, together with higher time delays of the hip. Furthermore, the elderly adapted their reflexive stiffness around the ankle joint less compared with young participants. These results imply that elderly were stiffer and were less able to adapt to external force fields.

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Koike, Yasuharu, and Mitsuo Kawato. "Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model." Biological Cybernetics 73, no. 4 (September 1995): 291–300. http://dx.doi.org/10.1007/bf00199465.

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Koike, Yasuharu, and Mitsuo Kawato. "Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model." Biological Cybernetics 73, no. 4 (September 1, 1995): 291–300. http://dx.doi.org/10.1007/s004220050185.

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Marín, Javier, and José J. Marín. "Forces: A Motion Capture-Based Ergonomic Method for the Today’s World." Sensors 21, no. 15 (July 29, 2021): 5139. http://dx.doi.org/10.3390/s21155139.

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Approximately three of every five workers are affected by musculoskeletal disorders, especially in production environments. In this regard, workstation ergonomic evaluations are especially beneficial for conducting preventive actions. Nevertheless, today’s context demonstrates that traditional ergonomic methods should lead to smart ergonomic methods. This document introduces the Forces ergonomic method, designed considering the possibilities of inertial motion capture technology and its applicability to evaluating actual workstations. This method calculates the joint risks for each posture and provides the total risk for the assessed workstation. In this calculation, Forces uses postural measurement and a kinetic estimation of all forces and torques that the joints support during movement. This paper details the method’s fundamentals to achieve structural validity, demonstrating that all parts that compose it are logical and well-founded. This method aims to aid prevention technicians in focusing on what matters: making decisions to improve workers’ health. Likewise, it aims to answer the current industry needs and reduce musculoskeletal disorders caused by repetitive tasks and lower the social, economic, and productivity losses that such disorders entail.

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Gutierrez-Giles, Alejandro, Miguel A. Padilla-Castañeda, Luis Alvarez-Icaza, and Enoch Gutierrez-Herrera. "Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation." Sensors 22, no. 22 (November 10, 2022): 8670. http://dx.doi.org/10.3390/s22228670.

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

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D’Amico, Moreno, Edyta Kinel, Gabriele D’Amico, and Piero Roncoletta. "A Self-Contained 3D Biomechanical Analysis Lab for Complete Automatic Spine and Full Skeleton Assessment of Posture, Gait and Run." Sensors 21, no. 11 (June 7, 2021): 3930. http://dx.doi.org/10.3390/s21113930.

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Quantitative functional assessment of Posture and Motion Analysis of the entire skeleton and spine is highly desirable. Nonetheless, in most studies focused on posture and movement biomechanics, the spine is only grossly depicted because of its required level of complexity. Approaches integrating pressure measurement devices with stereophotogrammetric systems have been presented in the literature, but spine biomechanics studies have rarely been linked to baropodometry. A new multi-sensor system called GOALS-E.G.G. (Global Opto-electronic Approach for Locomotion and Spine-Expert Gait Guru), integrating a fully genlock-synched baropodometric treadmill with a stereophotogrammetric device, is introduced to overcome the above-described limitations. The GOALS-EGG extends the features of a complete 3D parametric biomechanical skeleton model, developed in an original way for static 3D posture analysis, to kinematic and kinetic analysis of movement, gait and run. By integrating baropodometric data, the model allows the estimation of lower limb net-joint forces, torques and muscle power. Net forces and torques are also assessed at intervertebral levels. All the elaborations are completely automatised up to the mean behaviour extraction for both posture and cyclic-repetitive tasks, allowing the clinician/researcher to perform, per each patient, multiple postural/movement tests and compare them in a unified statistically reliable framework.

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Minh, Vu Trieu, Mart Tamre, Victor Musalimov, Pavel Kovalenko, Irina Rubinshtein, Ivan Ovchinnikov, David Krcmarik, Reza Moezzi, and Jaroslav Hlava. "Model Predictive Control for Modeling Human Gait Motions Assisted by Vicon Technology." Journal Européen des Systèmes Automatisés 53, no. 5 (November 15, 2020): 589–600. http://dx.doi.org/10.18280/jesa.530501.

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Human muscles and the central nervous system (CNS) play the key role to control the human movements and activities. The human CNS determines each human motion following three steps: estimation of the movement trajectory; calculation of required energy for muscles; then perform the motion. In these three step tasks, the human CNS determines the first two steps and the human muscles conduct the third one. This paper efforts the use of model predictive control (MPC) algorithm to simulate the human CNS calculation in the case of gait motion. We first build up the human gait motion mathematical model with 5-link mechanism. This allows us to apply MPC to calculate the optimal torques at each joint and optimal trajectory for muscles. Outcomes of simulations simultaneously are compared with the real human movements captured by the Vicon motion capture technology which is the novelty of this study. Results show that tracking errors are not excessed 7%.

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Dal Maso, Fabien, Mickaël Begon, and Maxime Raison. "Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models." Journal of Applied Biomechanics 33, no. 1 (February 2017): 80–86. http://dx.doi.org/10.1123/jab.2016-0062.

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One approach to increasing the confidence of muscle force estimation via musculoskeletal models is to minimize the root mean square error (RMSE) between joint torques estimated from electromyographic-driven musculoskeletal models and those computed using inverse dynamics. We propose a method that reduces RMSE by selecting subsets of combinations of maximal voluntary isometric contraction (MVIC) trials that minimize RMSE. Twelve participants performed 3 elbow MVIC in flexion and in extension. An upper-limb electromyographic-driven musculoskeletal model was created to optimize maximum muscle stress and estimate the maximal isometric force of the biceps brachii, brachialis, brachioradialis, and triceps brachii. Maximal isometric forces were computed from all possible combinations of flexion-extension trials. The combinations producing the smallest RMSE significantly reduced the normalized RMSE to 7.4% compared with the combination containing all trials (9.0%). Maximal isometric forces ranged between 114–806 N, 64–409 N, 236–1511 N, and 556–3434 N for the brachii, brachialis, brachioradialis, and triceps brachii, respectively. These large variations suggest that customization is required to reduce the difference between models and actual participants’ maximal isometric force. While the smallest previously reported RMSE was 10.3%, the proposed method reduced the RMSE to 7.4%, which may increase the confidence of muscle force estimation.

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Guelton, Kevin, Sébastien Delprat, and Thierry-Marie Guerra. "An alternative to inverse dynamics joint torques estimation in human stance based on a Takagi–Sugeno unknown-inputs observer in the descriptor form." Control Engineering Practice 16, no. 12 (December 2008): 1414–26. http://dx.doi.org/10.1016/j.conengprac.2008.04.002.

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Yang, Jiantao, and Yuehong Yin. "Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton." Sensors 20, no. 13 (June 30, 2020): 3685. http://dx.doi.org/10.3390/s20133685.

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Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP.

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Bae, Joonbum, Kyoungchul Kong, and Masayoshi Tomizuka. "Real-time Estimation of Lower Extremity Joint Torques in Normal Gait* *This work was supported by National Science Foundation (NSF) under Grant CMMI-0800501." IFAC Proceedings Volumes 42, no. 16 (2009): 443–48. http://dx.doi.org/10.3182/20090909-4-jp-2010.00076.

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Lee, Myunghyun, and Sukyung Park. "Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum." Sensors 20, no. 21 (November 4, 2020): 6277. http://dx.doi.org/10.3390/s20216277.

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Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of mass (CoM) in the three dimensions (3D). This biomechanical characteristic can be used to establish an appropriate input and structure of an ANN. In this study, an ANN for estimating gait kinetics with a single inertial measurement unit (IMU) was designed; the kinematics of the IMU placed on the sacrum as a proxy for the CoM kinematics were applied based on the 3D spring mechanics. The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU.

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Narayan, Jyotindra, and Santosha Kumar Dwivedy. "Preliminary design and development of a low-cost lower-limb exoskeleton system for paediatric rehabilitation." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 235, no. 5 (February 16, 2021): 530–45. http://dx.doi.org/10.1177/0954411921994940.

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In this work, the design, modeling, and development of a low-cost lower limb exoskeleton (LLES) system are presented for paediatric rehabilitation (age: 8–12 years, mass: 25–40 kg, height: 115–125 cm). The exoskeleton system, having three degrees-of-freedom (DOFs) for each limb, is designed in the SolidWorks software. A wheel support module is introduced in the design to ensure the user’s stability and safety. The finite element analysis of the hip joint connector along with the wheel support module is realized for maximum loading conditions. The holding torque capacity of exoskeleton joints is estimated using an affordable spring-based experimental setup. A working prototype of the LLES is developed with holding torque rated actuators. Thereafter, the dynamic analysis for the human-exoskeleton coupled system is carried out using the Euler-Lagrange principle and SimMechanics model. The simulation results of estimating joint actuator torques are obtained for two paraplegic subjects (Case I: 10 years age, 30 kg mass, 120 cm height and Case II: 12 years age, 40 kg mass, 125 cm height). The details of input parameters such as body mass, link lengths, joint angles, and contact forces are discussed. The simulation results of dynamic analysis have shown the potential of estimating the torques of joint actuators for the developed prototype during motion assistance and gait rehabilitation.

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Simoni, Luca, Manuel Beschi, Giovanni Legnani, and Antonio Visioli. "Modelling the temperature in joint friction of industrial manipulators." Robotica 37, no. 5 (November 10, 2017): 906–27. http://dx.doi.org/10.1017/s0263574717000509.

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SummaryIn this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed.

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Sarmiento-Ramos, Jose Luis, Andrés Felipe Meneses-Castro, and Pedro José Jaimes-Mantilla. "Dynamic Model of Lower Limb Motion in the Sagittal Plane during the Gait Cycle." Ingeniería 29, no. 1 (January 13, 2024): e20333. http://dx.doi.org/10.14483/23448393.20333.

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Context: This work presents the development of a dynamic model for human lower limb motion in the sagittal plane during the gait cycle. The primary objective of this model is to serve as a powerful tool for the design of rehabilitation and assistive devices, such as exoskeletons, prostheses, and orthoses. It achieves this by facilitating the estimation of joint torques, the detailed analysis of kinematic variables, optimal actuator selection, and the exploration of advanced control techniques. Method: The dynamic model consists of two primary components: (1) the plant model and (2) a closed-loop controller. The plant model represents the forward dynamics of human gait and is based on a multi-mass pendulum composed of three segments of the lower limb (thigh, lower leg, and foot) and three joints (hip, knee, and ankle). It is analyzed using the Euler-Lagrange formulation and the nonlinear second-order differential equations are implemented in MATLAB’s Simulink. To reproduce reference human gait trajectories and simulate the functioning of the neuromusculoskeletal system and the central nervous system, a closed-loop PID controller is incorporated into the plant model. It is noteworthy that the scope of this dynamic model is specifically confined to the sagittal plane. Results: The dynamic model is evaluated in terms of angular displacement tracking using the relative maximum error (RME) and the root mean square error (RMSE) for reference trajectories of healthy adult male human gait as reported in the literature. The model demonstrates tracking with errors below 2.2 [°] in magnitude and 3,5% for all three considered segments (thigh, lower leg, and foot). Conclusions: The quantitative results show that the dynamic model developed in this work is reliable and allows for a precise reproduction of human gait trajectories.

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Coelho, Jefferson, Marcela Machado, and Maciej Dutkiewicz. "Estimation of loosening torque in bolted joints from experimental data and regression models." Journal of Physics: Conference Series 2909, no. 1 (December 1, 2024): 012016. https://doi.org/10.1088/1742-6596/2909/1/012016.

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Abstract Structures are often joined using fasteners such as rivets or bolts, which are chosen based on their ability to meet performance requirements. Bolts are popular due to their advantages, such as avoiding movement and ensuring the stability and security of bolted joints. However, one of the main issues with fasteners is loosening, which can be caused by shock and vibration and lead to serious damage and structural failure. The use of machine learning techniques for bolt joint verification is limited. Hence, this study proposes a machine learning workflow centred on estimating torque by analysing raw spectral signals derived from experimental tests. This approach accounts for intrinsic variabilities in torque estimation and enhances our results performance. To enrich the experimental dataset, the study employs a technique involving the generation of synthetic datasets based on statistical moments derived from experimental data. Results show that machine learning can estimate torque in joint structures based on data collected under various conditions, improving performance with the lowest error rate.

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Kawase, Toshihiro, Hiroyuki Kambara, and Yasuharu Koike. "A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals." Journal of Robotics and Mechatronics 24, no. 1 (February 20, 2012): 205–18. http://dx.doi.org/10.20965/jrm.2012.p0205.

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In some researches about power assist devices, surface ElectroMyoGraphy (EMG) signals are used to estimate user intentions to move their limbs. These conventional methods mainly focus on estimation of joint torque. However, the devices based on torque estimation are inclined to cause the vibration of users’ posture originating from the waviness of the EMG signals. Focusing on estimation of states related to the joint angle may improve the performance of the power assist devices. This paper proposes a new method that estimates user joint equilibrium point and stiffness separately from the EMG and that amplifies the stiffness while tuning the device joints according to user equilibrium points. To evaluate the method, we constructed a power assist system for the wrist and compared the method with a method based on simple torque estimation during posture maintenance tasks. Our results showed that the proposed method offers a more stable operation at the same assist ratio and proved the effectiveness of the method.

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Xiong, Gen-Liang, Hai-Chu Chen, Jing-Xin Shi, and Fa-Yun Liang. "Joint torque control of flexible joint robots based on sliding mode technique." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141984671. http://dx.doi.org/10.1177/1729881419846712.

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For robots with flexible joints, the joint torque dynamics makes it difficult to control. An effective solution is to carry out a joint torque controller with fast enough dynamic response. This article is dedicated to design such a torque controller based on sliding mode technique. Three joint torque control approaches are proposed: (1) The proportional-derivative (PD)-type controller has some degree of robustness by properly selecting the control gains. (2) The direct sliding mode control approach which fully utilizes the physical properties of electric motors. (3) The sliding mode estimator approach was proposed to compensate the parameter uncertainties and the external disturbances of the joint torque system. These three joint torque controllers are tested and verified by the simulation studies with different reference torque trajectories and under different joint stiffness.

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Li, Xinwei, Su Liu, Ying Chang, Sujiao Li, Yuanjie Fan, and Hongliu Yu. "A Human Joint Torque Estimation Method for Elbow Exoskeleton Control." International Journal of Humanoid Robotics 17, no. 03 (March 11, 2020): 1950039. http://dx.doi.org/10.1142/s0219843619500397.

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Exoskeleton for motion assistance has obtained more and more attention due to its advantages in rehabilitation and assistance for daily life. This research designed an estimation method of human joint torque by the kinetic human–machine interaction between the operator’s elbow joint torque and the output of exoskeleton. The human elbow joint torque estimation was obtained by back propagation (BP) neural network with physiological and physical input elements including shoulder posture, elbow joint-related muscles activation, elbow joint position, and angular velocity. An elbow-powered exoskeleton was developed to verify the validity of the human elbow joint torque estimation. The average correlation coefficients of estimated and measured three shoulder joint angles are 97.9%, 96.2%, and 98.1%, which show that estimated joint angles are consistent with the measured joint angle. The average root-mean-square error between estimated elbow joint torque and measured values is about 0.143[Formula: see text]N[Formula: see text]m. The experiment results proved that the proposed strategy had good performance in human joint torque estimation.

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Liu, Xun, Yaqiu Liu, and Hanchen Zhao. "A Redundant Manipulator Joint Torque Estimation Method Based on Disturbance Observer." International Journal on Artificial Intelligence Tools 29, no. 07n08 (November 30, 2020): 2040015. http://dx.doi.org/10.1142/s0218213020400151.

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With the continuous development of the robot industry, both industrial robots and collaborative robots are developing towards light type and intelligence. The core issue is that how to improve the dynamic control performance of robots and reduce costs. The accurate torque feedback control can be achieved by introducing a joint torque sensor. The disadvantages brought by it are higher cost and the limited performance of the torque sensor. Therefore, on the basis of the traditional current estimated torque, combined with the accurate joint torque data fed back by the torque sensor, a method to estimate the harmonic transmission torque in the joint based on the disturbance observer is proposed, and a joint torque model is constructed. At the same time, the compensation factor is introduced to improve the accuracy of torque estimation. In the method proposed in this paper, the theoretical position and actual position, speed difference and motor current of the dual encoder on the motor side and the link side are used to estimate the harmonic transmission torque through the disturbance observer, and the corresponding coefficient is identified. By calibrating the transmission error compensation term and friction force with the torque sensor, the joint torque estimation model is obtained, and the sensorless joint torque estimation can be realized. This method does not require additional torque error compensation caused by harmonic drive deformation in the controller. Therefore, the torque control method without torque sensor is adopted in batch, which is not affected by the configuration and dynamic parameters of the manipulator. In the experiment, the output data of the joint torque sensor is used for testing and comparison. Through the single joint and redundant robot manipulator integration testing, the effectiveness of the proposed joint torque estimation method is verified.

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Li, Zhan, Hong Cheng, Hongliang Guo, and Xiaohong Sun. "Compliant training control of ankle joint by exoskeleton with human EMG-torque interface." Assembly Automation 37, no. 3 (August 7, 2017): 349–55. http://dx.doi.org/10.1108/aa-12-2016-161.

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Purpose The purpose of this paper is to make compliant training control of exoskeleton for ankle joint with electromyograph (EMG)-torque interface. Design/methodology/approach A virtual compliant mapping which is modeled by mass-spring-damper system is incorporated into the whole system at the reference input. The EMG-torque interface contains both data acquisition and torque estimator/predictor, and extreme learning machine is utilized for joint torque estimation/prediction from multiple channels of EMG signals. Findings The reference ankle joint angle to follow is produced from the compliance mapping whose input is the measured/predicted torque on healthy subjects. The control system works well with the desired angle to track. In the actuation level, the input torque to drive the ankle exoskeleton is less than the actual torque of the subject(s). This may have positive influence on diminishing overshoot of input torque from motors and protect the actuators. The torque prediction and final tracking control performance demonstrate the efficiency of the presented architecture. Originality/value This work can be beneficial to compliant training of ankle exoskeleton system for pilots and enhance current training control module in rehabilitation.

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Journal articles on the topic 'Joint torques estimation'

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