Neural networks

A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network

With the continuous improvement of automation, industrial robots have become an indispensable part of automated production lines. They widely used in a number of industrial production activities, such as spraying, welding, handling, etc., and have a great role in these sectors. Recently, the robotic technology is developing towards high precision, high intelligence. Robot calibration technology has a great significance to improve the accuracy of robot. However, it has much work to be done in the identification of robot parameters. The parameter identification work of existing serial and parallel robots is introduced. On the one hand, it summarizes the methods for parameter calibration and discusses their advantages and disadvantages. On the other hand, the application of parameter identification is introduced. This overview has a great reference value for robot manufacturers to choose proper identification method, points further research areas for researchers. Finally, this paper analyzes the existing problems in robot calibration, which may be worth researching in the future.

An overview of calibration technology of industrial robots

This paper proposes a laser tracker based kinematic calibration of a 3-degree-of-freedom (DoF) rotational parallel manipulator that would be applied in tracking and positioning fields. The process is implemented in this paper by four steps: 1) formulation of the geometric error model of this manipulator by means of screw theory considering all possible geometric source errors, which is followed by the verification of this error model employing SolidWorksź software. 2) sensitivity analysis of all geometric source errors based upon Monte Carlo method and remove some errors that have little influence on the pose accuracy of the moving platform in order to decrease the difficulty and complexity of the kinematic calibration. 3) error parameter identification and kinematic calibration experiment using laser tracker. 4) error compensation by amending controller model. Kinematic calibration experiment results of this 3-DoF rotational parallel manipulator show that three angular deviations are improved from 1.97°, 0.24° and 1.75° to 0.53°, 0.10° and 0.19° respectively within the prescribed workspace. Geometric error modeling based on screw theory is presented.The geometric error model is verified based upon the software method.Calibration includes sensitivity analysis, measurement plan and error identification.Software simulation and experiment are given to verify kinematic calibration flow.

Kinematic calibration of a 3-DoF rotational parallel manipulator using laser tracker

Industrial robot provides an optimistic alternative of traditional CNC machine tool due to its advantages of large workspace, low cost and great flexibility However, the low posture-dependent stiffness deteriorates the machining accuracy in robotic milling tasks To increase the stiffness, this paper introduces a pose optimization method for the milling robot when converting a five-axis CNC tool path to a commercial six-axis industrial robot trajectory, taking advantage of a redundant degree of freedom First, considering the displacements of at least three points on the end effector of the robot, a new frame-invariant performance index is proposed to evaluate the stiffness of the robot at a certain posture Then, by maximizing this index, a one-dimensional posture optimization problem is formulated in consideration of the constraints of joint limits, singularity avoidance and trajectory smoothness The problem is solved by a simple discretization search algorithm Finally, the performance index and the robot trajectory optimization algorithm are validated by simulations and experiments on an industrial robot, showing that the machining accuracy can be efficiently improved by the proposed method

Stiffness-based pose optimization of an industrial robot for five-axis milling

Owing to the flexibility of robot joints and links, the industrial robot can hardly achieve the specified accuracy to perform tasks when a payload is attached at its end-effector. Hence, compliance errors due to the deflections of robot joints and links should be identified and compensated as well as kinematic errors to improve the positioning accuracy of the industrial robot. Assuming the links of the robot are much stiffer than its actuated joints, compliance errors mainly come from the joint deflections. This paper presents a new algorithm for robot calibration, which simultaneously identifies joint compliance and kinematic parameters of industrial robots through a comprehensive error model consisting of kinematic errors and compliance errors. The identified parameters are used to compensate kinematic errors and compliance errors in industrial robots. The proposed calibration algorithm is implemented on a 6 degree of freedom serial robot (Hyundai YS100) to show its effectiveness.

Simultaneous identification of joint compliance and kinematic parameters of industrial robots

The robustness of robot calibration regarding the measurement noise is sensitive to the measurement poses serving as constraints on the parameters to be estimated. This paper presents a pose select...

https://journals.sagepub.com/doi/pdf/10.1155/2014/291389

Selecting Optimal Measurement Poses for Kinematic Calibration of Industrial Robots

Due to the flexibility of robot joints and links, industrial robots can hardly achieve the accuracy required to perform tasks when a payload is attached at their end-effectors. This article present...

https://journals.sagepub.com/doi/pdf/10.1177/1687814015590289

A hybrid least-squares genetic algorithm–based algorithm for simultaneous identification of geometric and compliance errors in industrial robots

In this paper, comparisons of the 4 observability indices for robot calibration considering joint stiffness parameters are performed for finding the best robot pose set. 4 observability indices such as the minimum singular value index, the inverse condition number index, the product of singular values index and the noise amplification index were discussed and compared with the conventional calibration Jacobian matrix. Here, we propose the joint stiffness included Jacobian to consider robot joint deflection due to the load. Those indices are evaluated and compared in light of the resulting calibration accuracies. Genetic algorithms customized for a simple 2-link planar robot manipulator with a loading in the end-effector are applied to search optimal robot poses where measurement values are used to calibrate both the robot kinematic parameters and joint stiffness parameters. In this process, 4 observability indices, treated as fitness functions respectively, are evaluated and compared through a 2-link robot manipulator calibration simulation. While the kinematic parameters are well calibrated in all of the 4 observability, in joint stiffness included identification, these 4 observabilities perform differently. The noise amplification index identifies joint stiffness more stably and approximately with very small residual errors than others.

Jian Zhou

Papers

A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network

Simultaneous identification of joint compliance and kinematic parameters of industrial robots

Selecting Optimal Measurement Poses for Kinematic Calibration of Industrial Robots

A hybrid least-squares genetic algorithm–based algorithm for simultaneous identification of geometric and compliance errors in industrial robots

Comparison of the Observability Indices for Robot Calibration considering Joint Stiffness Parameters