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Dynamic Visual Servoing for Fast Robotics Arms

Abstract : This thesis deals with increasing the productivity in manufacturing robots, when performing sensor-based tasks. Such tasks may be coming from the target not being absolutely positioned. Visual servoing control schemes are well known for their robustness and precision, but generally require long execution times due to differentfactors.Control laws are generally formulated only at a kinematic level and characterized by exponentially decreasing velocities. Moreover, the nonlinear map from the operational space to the sensor space can lead to sub-optimal and longer paths. To increase control performances and reduce the time required to complete a task, this thesis investigates the use of second-order interaction models. Their use in dynamic feedback control laws is investigated and compared to classical controllers. They are then employed in Model Predictive Control (MPC) schemes, allowing to obtain higher velocities and better sensor trajectories. However, a drawback of MPC techniques is their computational load. In order to obtain even better results, a new type of predictive control is thus investigated, leading to a reduced number of variables involved in MPC optimization problems thanks to the use of a parameterization of the control input sequences.
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Submitted on : Wednesday, March 24, 2021 - 10:54:09 AM
Last modification on : Monday, June 27, 2022 - 3:03:20 AM


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  • HAL Id : tel-03179207, version 1


Franco Fusco. Dynamic Visual Servoing for Fast Robotics Arms. Automatic. École centrale de Nantes, 2020. English. ⟨NNT : 2020ECDN0031⟩. ⟨tel-03179207⟩



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