Showing papers by "Mojtaba Ahmadieh Khanesar published in 2023"

Intelligent Static Calibration of Industrial Robots using Artificial Bee Colony Algorithm

Abstract: This paper proposes an industrial robot calibration methodology using an artificial bee colony algorithm. Open loop industrial robot positions are usually calculated using joint angle readings and industrial robot forward kinematics, where feedback control systems are then use iteratively to improve performance. This can often be time consuming and risks unstable control, so the preference is to enable as accurate open loop control as possible. Industrial robot forward kinematics include Denavit-Hartenberg (DH) parameters. However, assembly and manufacturing tolerances may result in differences between actual and nominal DH parameters. To improve industrial robot positional accuracies, it is required to better estimate its DH parameters. A highly accurate laser tracker system provides the positional measurement required to perform calibration of the DH parameters. For this purpose, a Leica AT960-MR, a laser tracker which works based on interferometry principles, is used to provide end effector 3D position measurements. An artificial Bee colony algorithm is then used to improve the cost function associated with the forward kinematic error by estimating more accurate industrial robot DH parameters. The implementation results demonstrate that using calibrated industrial robot DH parameters, it is possible to improve the open loop positional accuracies of the robot compared to uncalibrated forward kinematics mean absolute error for test data from 75.4 $\mu$m to 60.1 $\mu$m (20.3% improvement).

Precision Denavit–Hartenberg Parameter Calibration for Industrial Robots Using a Laser Tracker System and Intelligent Optimization Approaches

Abstract: Precision object handling and manipulation require the accurate positioning of industrial robots. A common practice for performing end effector positioning is to read joint angles and use industrial robot forward kinematics (FKs). However, industrial robot FKs rely on the robot Denavit–Hartenberg (DH) parameter values, which include uncertainties. Sources of uncertainty associated with industrial robot FKs include mechanical wear, manufacturing and assembly tolerances, and robot calibration errors. It is therefore necessary to increase the accuracy of DH parameter values to reduce the impact of uncertainties on industrial robot FKs. In this paper, we use differential evolution, particle swarm optimization, an artificial bee colony, and a gravitational search algorithm to calibrate industrial robot DH parameters. A laser tracker system, Leica AT960-MR, is utilized to register accurate positional measurements. The nominal accuracy of this non-contact metrology equipment is less than 3 μm/m. Metaheuristic optimization approaches such as differential evolution, particle swarm optimization, an artificial bee colony and a gravitational search algorithm are used as optimization methods to perform the calibration using laser tracker position data. It is observed that, using the proposed approach with an artificial bee colony optimization algorithm, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three dimensions for the test data decreases from its measured value of 75.4 μm to 60.1 μm (a 20.3% improvement).

Enhancing Positional Accuracy of the XY-Linear Stage Using Laser Tracker Feedback and IT2FLS

Abstract: This paper proposes a calibration algorithm to improve the positional accuracies of an industrial XY-linear stage. Precision positioning of these linear stages is required to maintain highly accurate object handling and manipulation. However, due to imprecisions in linear motor stages and the gearbox, static and dynamic errors exist within these manipulators that cannot be adjusted internally. In this paper, to improve the positioning accuracy of these manipulators, measurements from a laser tracker are used within an interval type-2 fuzzy logic system. The laser tracker used in this experiment is an AT960-MR, which is a highly accurate noncontact coordinate metrology equipment capable of performing highly accurate robotic measurements. To perform calibration, we use an IT2FLS to find a nonlinear correcting relationship to compensate for position errors. The IT2FLS acts on the commands given to the move stage to find the accurate position of the move stage. To train the IT2FLS, we use particle swarm optimization (PSO) for the antecedent part parameters and Moore–Penrose generalized inverse to estimate the consequent part parameters. Data are split into train/test data to test the efficacy of the proposed algorithm. It is shown that by using the proposed IT2FLS-based calibration approach, the standard deviation of the position errors can be decreased from 86.1μm to 55.9μm, which is a 35.1% improvement. Comparison results with a multilayer perceptron neural network reveal that the proposed IT2FLS-based calibration algorithm outperforms multilayer perceptron neural network for positional calibration purposes.