857 research outputs found
Uncalibrated Dynamic Mechanical System Controller
An apparatus and method for enabling an uncalibrated, model independent controller for a mechanical system using a dynamic quasi-Newton algorithm which incorporates velocity components of any moving system parameter(s) is provided. In the preferred embodiment, tracking of a moving target by a robot having multiple degrees of freedom is achieved using an uncalibrated model independent visual servo control. Model independent visual servo control is defined as using visual feedback to control a robot's servomotors without a precisely calibrated kinematic robot model or camera model. A processor updates a Jacobian and a controller provides control signals such that the robot's end effector is directed to a desired location relative to a target on a workpiece.Georgia Tech Research Corporatio
Adaptive Finite-Time Model Estimation and Control for Manipulator Visual Servoing using Sliding Mode Control and Neural Networks
The image-based visual servoing without models of system is challenging since
it is hard to fetch an accurate estimation of hand-eye relationship via merely
visual measurement. Whereas, the accuracy of estimated hand-eye relationship
expressed in local linear format with Jacobian matrix is important to whole
system's performance. In this article, we proposed a finite-time controller as
well as a Jacobian matrix estimator in a combination of online and offline way.
The local linear formulation is formulated first. Then, we use a combination of
online and offline method to boost the estimation of the highly coupled and
nonlinear hand-eye relationship with data collected via depth camera. A neural
network (NN) is pre-trained to give a relative reasonable initial estimation of
Jacobian matrix. Then, an online updating method is carried out to modify the
offline trained NN for a more accurate estimation. Moreover, sliding mode
control algorithm is introduced to realize a finite-time controller. Compared
with previous methods, our algorithm possesses better convergence speed. The
proposed estimator possesses excellent performance in the accuracy of initial
estimation and powerful tracking capabilities for time-varying estimation for
Jacobian matrix compared with other data-driven estimators. The proposed scheme
acquires the combination of neural network and finite-time control effect which
drives a faster convergence speed compared with the exponentially converge
ones. Another main feature of our algorithm is that the state signals in system
is proved to be semi-global practical finite-time stable. Several experiments
are carried out to validate proposed algorithm's performance.Comment: 24 pages, 10 figure
Transferring visuomotor learning from simulation to the real world for robotics manipulation tasks
Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot
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