Abstract
For a long time, the field of machining has been dominated by computer numerical control (CNC) machines as they are simple from the kinematic point of view and stiff that ensures high positioning accuracy. However they are very expensive and occupy large space, thus there is a demand from industry for cheaper and smaller alternatives. The most promising one is an industrial manipulator which is indeed cheaper and have the lower footprint to workspace ration. The reason why industrial manipulators have not yet replaced CNC machines is their comparatively low stiffness that causes deflections in the end-effector position and orientation due to an external force applied to it during a machining operation. Therefore, an extensive research is being conducted in this area that focuses on developing accurate stiffness model of the robot and incorporating it into the control scheme. As the majority of stiffness models include stiffness of the links as well as joints even though the former cannot be obtained from the parameters provided by the robot manufacturer, that is why it is important to understand how accurately a reduced stiffness model, which lumps all the stiffness properties in the joints, can replicate the deflections of the full model. In this paper, we focus on analyzing the quantitative difference between these two models using Virtual Joint Modeling method and its effect on the trajectory tracking. The systematic analysis carried out for FANUC R-2000iC/165F robot demonstrates that reduced stiffness model can quite accurately replicate the full one so that up to 92% of the end-effector deflection can be compensated. The average deflection error after compensation is about 0.7 \(\upmu \text {m}/\text {N}\) for a typical heavy industrial robot under the loading.
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This research has been supported by the grant of Russian Science Foundation 17-19-01740.
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Mamedov, S., Popov, D., Mikhel, S., Klimchik, A. (2020). Increasing Machining Accuracy of Industrial Manipulators Using Reduced Elastostatic Model. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-030-31993-9_19
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