Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots

Ma, Lei; Bazzoli, Patrick; Sammons, Patrick M.; Landers, Robert G.; Bristow, Douglas A.

doi:10.1016/j.rcim.2017.09.006

Cited by 113 publications

(51 citation statements)

References 15 publications

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“…whereθ i is the nominal robot joint variable and C i (θ i ) represents the compensation factor for non-kinematic errors. We can use Chebyshev polynomials to calculate C i (θ i ) [16]:…”

Section: Of 14mentioning

confidence: 99%

“…Various correction methods have been developed to mitigate the influence of non-kinematic errors. Ma et al demonstrated the importance of non-kinematic errors and generalized them by a representation with error matrices containing high-order Chebyshev polynomials that reflect individual error terms [16]. Whitney et al developed a forward calibration method using joint encoder offset, link length, and consecutive-axis relative orientations as parameters and experimentally evaluated the effects of joint backlash, gear transmission, and compliance errors [17].…”

Section: Introductionmentioning

confidence: 99%

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Absolute Positioning Accuracy Improvement in an Industrial Robot

Jiang

Jia

et al. 2020

Sensors

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The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

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“…whereθ i is the nominal robot joint variable and C i (θ i ) represents the compensation factor for non-kinematic errors. We can use Chebyshev polynomials to calculate C i (θ i ) [16]:…”

Section: Of 14mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Absolute Positioning Accuracy Improvement in an Industrial Robot

Jiang

Jia

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to compensate for the estimation error caused by neglecting the mass, damping, and friction of the cable in (12) and further approximate the system nonlinearity, it is necessary to model the estimation error for (24). Because the polynomial fitting is a standard and applicable method for nonlinear modeling [34], the polynomial fitting modeling is carried out by using the methods of Quasi-Newton and universal global optimization. In the cable-driven system, the actual rotation of the gripper jaw is closely related to the displacement of the driving and reset cables.…”

Section: B Motion Estimation Modeling Base On Ukfmentioning

confidence: 99%

UKF-Based Motion Estimation of Cable-Driven Forceps for Robot-Assisted Surgical System

Yan

et al. 2020

IEEE Access

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This paper presents an Unscented Kalman Filter (UKF)-based method to achieve highprecision motion estimation of cable-driven forceps for a robot-assisted surgical system. We analyze the operational/working principle of revolute joints of a 3-degree-of-freedom (3-DOF) cable-driven surgical manipulator. Then a gripper jaw is selected as a representative joint, which is actuated by a single-motor cable-driven mechanism with a reset spring. The corresponding system dynamics comprehend the mass, elasticity, damping, and friction of steel cables. By using the displacement and velocity of reset cable and the rotation angle of motor as observations, the motion estimation model based on UKF is derived. The estimation accuracy is verified experimentally, with the errors of absolute and root-mean-square (RMS) of less than 0.5 deg and 0.2 deg respectively. By comparisons with the least square methods (LSMs), the installation strategy of only one displacement sensor on the reset cable is determined, which is conducive to further refinements of the mechanism. Furthermore, the external force loading experiments are performed, with the RMS estimation error of less than 0.5 deg for the external force of no more than 250 g applied on the tip of the gripper jaw. These experimental results validate the motion estimation accuracy of cable-driven forceps, without requiring sensors on the end joints or slender tool shaft of surgical instruments. INDEX TERMS Robot-assisted surgical system, cable-driven surgical forceps, motion estimation, UKF-based method, external force loading.

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“…Several recently published studies-such as that by Wang et al (2012) used genetic algorithms to build mathematically calibrated equations to compensate the kinematic errors cylindrical-coordinate-based manipulator with three Degrees of Freedom (DOF). Ma et al (2018) conducted modelling and calibration of high-order joint-dependent kinematic errors for industrial robots. The process was performed using high-order Chebyshev polynomials to represent individual error terms and a laser tracker system to acquire the measured data.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Industrial Robot Kinematics Based on Results of Interpolating Error by Shape Function

Thu¹,

Quoc²,

Long³

2020

J. Eng. Appl. Sci.

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The initial accuracy of a robot arm depends not only on the hardware's manufacturing and assembly quality but also on control strategies. During assembly or regular maintenance, the kinematic accuracy of a robot arm needs to be verified and calibrated to maintain the desired quality. In this study, we present a method to determine and correct the robot kinematic error based on the shape function interpolation technique. Experimental research was applied to aABB robot with six degrees of freedom to verify the correctness of the method. The results indicated that the position errors of the end-effector were significantly reduced. The effectiveness of the proposed interpolation method and combined error compensation algorithm demonstrated that practical application is feasible.

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Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots

Cited by 113 publications

References 15 publications

Absolute Positioning Accuracy Improvement in an Industrial Robot

Absolute Positioning Accuracy Improvement in an Industrial Robot

UKF-Based Motion Estimation of Cable-Driven Forceps for Robot-Assisted Surgical System

Calibration of Industrial Robot Kinematics Based on Results of Interpolating Error by Shape Function

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