Identification of Robot Joint Torsional Stiffness Based on the Amplitude of the Frequency Response of Asynchronous Data

Xu, Kai; Liu, Xiaoqin; Wang, Dongxiao

doi:10.3390/machines9090204

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Different from the traditional industrial robot, where the joint parts of the manipulator need a certain torsional stiffness to complete grasping work, and to identify the torsional stiffness of the joint [11], in the field of soft robots, evolutionary algorithms are widely utilized to optimize the structure [12,13]. Sung et al [14] attached the joint part of the soft manipulator to the photo-responsive polymers and designed an anti-obstructing motion based on the nonlinear co-rotation finite element model, enabling the soft manipulator to grasp objects in the directly direction and avoid obstacles through multiscale simulations and structural designs.…”

Section: Introductionmentioning

confidence: 99%

An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

Zhang

et al. 2021

Machines

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Mitigating fatigue damage and improving grasping performance are the two main challenging tasks of applying the soft manipulator into industrial production. In this paper, the grasping position optimization-based control strategy is proposed for the soft manipulator and the corresponding characteristics are studied theoretically and experimentally. Specifically, based on the simulation, the resultant stress of step-function-type channels at the same pressure condition that was smallest compared with those of sine-function- and ramp-function-type channels, hence, a pneumatic network with step-function-type channels was selected for the proposed soft manipulator. Furthermore, in order to improve the grasping performance, the kinematics, mechanical, and grasping modeling for the soft manipulator were established, and a control strategy considering the genetic algorithm is introduced to detect the optimal position of the soft manipulator. The corresponding fabrication process and experiments were conducted to cross verify the results of the modeling and the control strategy. It is demonstrated that the internal pressure of the soft manipulator was reduced by 13.05% at the optimal position, which effectively helped mitigate the fatigue damage of the soft manipulator and prolonged the lifespan.

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Section: Introductionmentioning

confidence: 99%

An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

Zhang

et al. 2021

Machines

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Instantaneous Angular Speed Extraction Based on Nonuniform Local Polynomial Differentiator for the Stiffness Identification of the Robot Joint

Wang

Liu

et al. 2023

IEEE Trans. Instrum. Meas.

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Vibration Prediction of the Robotic Arm Based on Elastic Joint Dynamics Modeling

Wang

et al. 2022

Sensors

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The flexibility of the joint drive system of an industrial robot can cause vibration at the end part, which can lead to motion errors. A method to predict the vibration during the motion of the robot arm is proposed considering the robot joint flexibility. The method combines the internal transfer function of the drive system and the identification of parameters under external excitation. Firstly, the dynamics of the robot joint system are modeled by a double inertia elastic system. The joint system transfer function from the electromagnetic torque to the arm vibration is obtained according to the dynamics model. To solve the unknown parameters in the transfer function, a vibration dynamics model of the joint arm under the external forces on the arm is developed. According to this model, the equivalent stiffness, damping and load inertia of the joint can be obtained by the direct parametric method. Then, the vibration spectrum of the robot arm is derived from the motor electromagnetic torque and joint dynamics models were used to predict the vibration spectrum of the robot arm. The experiments were conducted on a single-joint robot testbed, and on an articulated industrial robot. In both experiments, the key parameters in the system were determined by impact experiments. Then, the vibration signal of the arm during the robot motion was obtained by electromagnetic torque prediction. The predicted vibration signals are analyzed in comparison with the actual vibration signals. The experimental results both show the validity of the vibration prediction.

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Identification of Robot Joint Torsional Stiffness Based on the Amplitude of the Frequency Response of Asynchronous Data

Cited by 3 publications

References 28 publications

An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

Instantaneous Angular Speed Extraction Based on Nonuniform Local Polynomial Differentiator for the Stiffness Identification of the Robot Joint

Vibration Prediction of the Robotic Arm Based on Elastic Joint Dynamics Modeling

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