On Evolving Fuzzy Modeling for Visual Control of Robotic Manipulators

In this paper, evolving takagi-sugeno fuzzy models are applied to model a dynamic system, e.g. a six degrees of freedom robotic manipulator. In recent years the dynamic model was obtained off-line, but when the robot performs tasks away from the modeled workspace, it fails in achieve the goal. Then, the models must evolve when the robot moves to a previously un-modeled workspace. The models were obtained online for uncalibrated visual servoing in 3D workspace. This approach will recursively update the inverse fuzzy models based only on measurements at a given time instant. The uncalibrated approach does not require calibrated kinematic and camera models, as needed in classical visual servoing to obtain the Jacobian. Experimental results obtained in a PUMA robot performing eye-to-hand visual servoing in 3D workspace are used to demonstrate the validity of the proposed approach, when compared to the previous developed off-line learning.