Robot Manipulation Control Using Jacobian Estimation

Resumen: The high performance of robots nowadays is a key point to develop position tasks with greater precision. For this reason manipulators are going beyond to a simple routine in daily industrial life. However, sometimes is difficult to know the exact dynamic system and commonly the uncertienties are not included in the complete mathematical form of the robot model, therefore the use of estimation available information like control signal and robot tip position allows to know factors which can be considered difficult to extract from the model. In this paper, an adaptive Kalman filter is computed to estimate Jacobian based on available information of the robot to deal with the robot control in the task space. Although the entries to Jacobian estimate may vary in a non-linear way, the adaptive Kalman filter get a linear and reasonable way of the Jacobian entries to task space control. The results obtained by the simulation show that the adaptive Kalman filter expose higher quality performance than conventional Kalman filter by tuning the covariance matrix and a fading factor.

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Josué Gómez, Chidentree Treesatayapun & América Morales. Robot Manipulation Control Using Jacobian Estimation. Memorias del Congreso Nacional de Control Automático, pp. 266-272, 2018.

Palabras clave

robot manipulator, Jacobian estimate, adaptive Kalman filter, task space control

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