Metoda ocenjevanja kinematičnih zmožnosti robota za optimizacijo lege obdelovanca v primeru robotske površinske obdelave : doktorska disertacija

slovenian

doktorska dela

2023

Decomposed Twist Feasibility (DTF) method   kinematic performance evaluation   manipulability index   manipulability polytope   motion decomposition   robot surface machining   task feasibility   task-dependent kinematic capability   workpiece placement optimization

Univerza v Mariboru, Fakulteta za elektrotehniko računalništvo in informatiko

Performing the machining of complex surfaces can be a challenging task for a robot, especially in terms of collaborative robotics, where the available motion capabilities are greatly reduced in comparison with conventional industrial robot arms. It is necessary to evaluate these capabilities prior to task execution, for which we need efficient algorithms, especially in the case of flexible robot applications. To provide accurate and physically consistent information about the maximum kinematic capabilities while considering the requirements of the task, an approach called the Decomposed Twist Feasibility (DTF) method is proposed in this doctoral thesis. The evaluation of the maximum feasible end-effector velocity is based on the idea of decomposition into the linear and angular motion capabilities, considering a typical robot machining task with synchronous linear and angular motion. The proposed DTF method is presented by the well-known manipulability polytope concept. Unlike the existing methods that estimate the kinematic performance capabilities in arbitrarily weighted twist space, or separately in the translation and the rotation subspace, our approach offers an accurate and simple solution for the determination of the total kinematic performance capabilities, which is often highly required, especially in the case of robot machining tasks. The numerical results obtained in this study show the effectiveness of the proposed approach in the case of the optimal placement of predefined tasks within the robot workspace.

07.08.2023