Robot position accuracy plays a very important role in advanced industrial applications. This article proposes a new method for enhancing robot position accuracy. In order to increase robot accuracy, the proposed method models and identifies determinable error sources, for instance, geometric errors and joint deflection errors. Because non-geometric error sources such as link compliance, gear backlash, and others are difficult to model correctly and completely, an artificial neural network is used for compensating for the robot position errors, which are caused by these non-geometric error sources. The proposed method is used for experimental calibration of an industrial Hyundai HH800 robot designed for carrying heavy loads. The robot position accuracy after calibration demonstrates the effectiveness and correctness of the method.
This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.
In this study, a fault-tolerant control (FTC) tactic using a sliding mode controller–observer method for uncertain and faulty robotic manipulators is proposed. First, a finite-time disturbance observer (DO) is proposed based on the sliding mode observer to approximate the lumped uncertainties and faults (LUaF). The observer offers high precision, quick convergence, low chattering, and finite-time convergence estimating information. Then, the estimated signal is employed to construct an adaptive non-singular fast terminal sliding mode control law, in which an adaptive law is employed to approximate the switching gain. This estimation helps the controller automatically adapt to the LUaF. Consequently, the combination of the proposed controller–observer approach delivers better qualities such as increased position tracking accuracy, reducing chattering effect, providing finite-time convergence, and robustness against the effect of the LUaF. The Lyapunov theory is employed to illustrate the robotic system’s stability and finite-time convergence. Finally, simulations using a 2-DOF serial robotic manipulator verify the efficacy of the proposed method.
Robot kinematic calibration used to be carried out with the partial pose measurements (position only) of dimension 3 in industry, while full pose measurements (orientation and position) of dimension 6 sometimes could be considered to improve the calibration performance. This paper investigates the effects of measurement dimensions on robot calibration accuracy. It compares the resulting robot accuracies in both partial pose and full pose cases after calibrating three structural types of robot manipulators such as a serial manipulator (Hyundai HA-06 robot), a single closed-chain manipulator (Hyundai HX-165 robot), and a multiple closed-chain manipulator (Hyundai HP-160 robot). These comparative studies show when the full-pose based calibration need to be considered and how much it contributes the improvement of robot accuracy to the different structural type of robot manipulators.
The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.
In this study, a manipulator calibration algorithm is suggested to decrease the positional errors of an industrial robotic manipulator using a genetic algorithm to select optimal measurement poses. First, a genetic algorithm based on the observability index is used for the selection of optimal measurement poses. By employing the selected optimal poses, conventional kinematic calibration is used to identify the geometric errors of the robot. Finally, to further improve the positional accuracy of the robot, compliance errors are compensated by a radial basis function neural network based on effective torques. The proposed method provides a novel and effective way to select optimal measurement poses for the calibration process using a genetic algorithm and enhances the accuracy of the robot manipulators by constructing a relationship between the effective torque and the compliance errors using a radial basis function. The results of the experimental calibration and validation processes carried out on a YS100 robot show the effectiveness of the proposed method in comparison with the other calibration approaches.
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