• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.11-19
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• 2014

In this paper, an external torque estimation problem in one-degree-of-freedom (1-DOF) flexible-joint robot equipped with a joint-torque sensor is revisited. Since a sensor torque from the joint-torque sensor is distorted by two dynamics having a spring connection, i.e., motor dynamics and link dynamics of a flexible-joint robot, a model-based estimation, rather than a simple linear spring model, should be required to extract external torques accurately. In this paper, an external torque estimation algorithm for a 1-DOF flexible-joint robot is proposed. This algorithm estimates both an actuating motor torque from the motor dynamics and an external link torque from the link dynamics simultaneously by utilizing the flexible-joint robot model and the Kalman filter estimation based on random-walk model. The basic structure of the proposed algorithm is explained, and the performance is investigated through a custom-designed experimental testbed for a vertical situation under gravity.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.26-32
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• 2016

A variable release torque-based compliance spring-clutch (VCSC) is presented. VCSC is a safe joint to reduce the impact of collisions between humans and robots. It is composed of four functional plates, balls, springs to make some functions in compliant movement, release mechanism, gravity compensation during its work. Also, it can estimate torque applied to a joint by using distance sensor and parameters of cam profile. The measured variable torque of prototype is 4.3~7.6 Nm and release torque is 4.3 Nm. In our future studies, a calibration for torque estimation will be conducted.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.55-62
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• 2018

In this paper, a method for estimation of external force on an end-effector using joint torque sensor is proposed. The method is based on portion of measure torque caused by external force. Due to noise in the torque measurement data from the torque sensor, a recursive least-square estimation algorithm is used to ensure a smoother estimation of the external force data. However it is inevitable to create a delay for the sensor to detect the external force. In order to reduce the delay, modified recursive least-square is proposed. The performance of the proposed estimation method is evaluated in an experiment on a developed six-degree-of-freedom robot. By using NI DAQ device and Labview, the robot control, data acquisition and The experimental results output are processed in real time. By using proposed modified RLS, the delay to estimate the external force with the RLS is reduced by 54.9%. As an experimental result, the difference of the actual external force and the estimated external force is 4.11% with an included angle of $5.04^{\circ}$ while in dynamic state. This result shows that this method allows joint torque sensors to be used instead of commonly used external sensory system such as F/T sensors.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.418-429
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• 2018

This paper studies the decentralized position/force control problem for constrained reconfigurable manipulator without torque sensing. A novel joint torque estimation scheme that exploits the existing structural elasticity of the manipulator joint with harmonic drive model is applied for each joint module. Based on the estimated joint torque and dynamic output feedback technique, a decentralized position/force control strategy is presented. In order to solve the problem of controller parameter perturbation, the non-fragile robust technique is introduced into the dynamic output feedback controller. Subsequently, the stability of the closed-loop system is proved using the Lyapunov theory and linear matrix inequality (LMI) technique. Finally, two 2-DOF constrained reconfigurable manipulators with different configurations are applied to verify the effectiveness of the proposed control scheme in numerical simulation.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1190-1193
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• 2004

The purpose of this study is to evaluate the possibility of identifying joint damping property through commercially available isokinetic ergometer (BIODEX). The proposed method is to estimate the damping torque of the knee joint from the difference between the external joint torque for maintaining isokinetic movement and the gravity torque of the lower leg. The damping torque was estimated at various joint angular velocities, from which the damping property would be derived. Measurement setup was composed of the BIODEX system with an external force sensor and Labview system. Matlab was used in the analysis of the damping property. The experimental result showed that the small variation in angular velocity due to acceleration and deceleration of the crank arm resulted in greater change of inertial torque than the damping torque, so that the estimation of damping property from the isokinetic movement is difficult.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.6
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• pp.650-657
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• 2013

This study uses a muscle activation sensor and elbow joint model to develop an estimation algorithm for human elbow joint torque for use in a human-robot interface. A modular-type MVS (Muscle Volume Sensor) and calibration algorithm are developed to measure the muscle activation signal, which is represented through the normalization of the calibrated signal of the MVS. A Hill-type model is applied to the muscle activation signal and the kinematic model of the muscle can be used to estimate the joint torques. Experiments were performed to evaluate the performance of the proposed algorithm by isotonic contraction motion using the KIN-COM$^{(R)}$ equipment at 5, 10, and 15Nm. The algorithm and its feasibility for use as a human-robot interface are verified by comparing the joint load condition and the torque estimated by the algorithm.

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.398-403
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• 2015

This paper introduces a method to estimate the contact-force of the leg of a walking robot and proposes a solution to a shortcoming of the previous study. This shortcoming was the deteriorating performance when estimating the contact-force whenever the rotation of each joint was reversed. It occurred because the friction-torque of each joint was not considered. In order to solve this problem, a friction-torque model for a robot leg was developed based on repetitive experimentation and used to improve the contact-force estimation performance. We verified the performance of the proposed method experimentally.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• Journal of Power System Engineering
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• v.12 no.2
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• pp.55-61
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• 2008

This paper discusses the advantages of using Fourier-based periodic excitation and of combining internal and external models in dynamic robot parameter identification. Internal models relate the joint torques or forces with the motion of the robot; external models relate the reaction forces and torques on the bedplate with the motion data. This combined model allows to combine joint torque/force and reaction torque/force measurements in one parameter estimation scheme. This combined model estimation will yield more accurate parameter estimates, and consequently better predictions of actuator torque, which is shown by means of a simulated experiment on a CRS A465 industrial robot.