Abstract
To achieve higher positioning accuracy, it is common practice to calibrate the robot. An essential part of the calibration is the estimation of the kinematic parameters. Due to various nonlinear influences on the end-effector position accuracy, such as joint and link flexibility, standard methods of identifying kinematic parameters do not always give a satisfactory result. In this paper, we propose a strategy that considers deflection-dependent errors to improve the overall positioning accuracy of the robot. As joint/link deflections mainly depend on gravity, we include the compensation of gravity-induced errors in the estimation procedure. In the first step of the proposed strategy, we compute the joint position errors caused by gravity. In the next step, we apply an existing optimization method to estimate the kinematic parameters. We propose to use an optimization based on random configurations. Such an approach allows good calibration even when we want to calibrate a robot in a bounded workspace. Since calibration is generally time consuming, we investigated how the number of measured configurations influences the calibration. To evaluate the proposed method, we used a simulation of the collaborative robot Franka Emika Panda in MuJoCo.
This work was supported by Slovenian Research Agency grant N2-0269 and P2-0076.
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Žlajpah, L., Petrič, T. (2023). Optimizing Robot Positioning Accuracy with Kinematic Calibration and Deflection Estimation. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-031-32606-6_30
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