Abstract
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.
Highlights
Industrial robots are flexible platforms and provide high repeatability for the automation of a variety of manufacturing tasks, a low absolute positioning accuracy may limit their applicability [1].Error sources in robots can be either kinematic or non-kinematic [2,3,4]
Non-kinematic errors caused by factors such as temperature variations, joint and link compliance, and gear backlash have a considerable effect on the absolute positioning accuracy
Given the difficulty to model non-kinematic errors, artificial neural networks have been used as an alternative to compensate the absolute positioning error [19]
Summary
Industrial robots are flexible platforms and provide high repeatability for the automation of a variety of manufacturing tasks, a low absolute positioning accuracy may limit their applicability [1]. Kinematic calibration effectively improves the absolute positioning accuracy of robots. This type of calibration comprises four steps: modeling, measurement, identification, and compensation or correction [7]. Given the difficulty to model non-kinematic errors, artificial neural networks have been used as an alternative to compensate the absolute positioning error [19]. (2) A neural network optimized using differential evolution is proposed to enhance the absolute positioning accuracy. The proposed network mitigates the effects of kinematic and non-kinematic errors and improves the absolute positioning accuracy of a robot. The proposed neural remainderby of differential this paper is organized follows: Thein influences and4,non-kinematic networkThe optimized evolutionasis detailed. The proposed neural network optimized experiments are performed for compensating the absolute positioning error.
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