Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators
Journal of Intelligent and Robotic Systems
Precise Positioning of Binocular Eye-to-Hand Robotic Manipulators
Journal of Intelligent and Robotic Systems
Image moments: a general and useful set of features for visual servoing
IEEE Transactions on Robotics
Statistically robust 2-D visual servoing
IEEE Transactions on Robotics
Learning robotic hand-eye coordination through a developmental constraint driven approach
International Journal of Automation and Computing
| Hi-index | 0.00 |
Vision based redundant manipulator control with a neural network based learning strategy is discussed in this paper. The manipulator is visually controlled with stereo vision in an eye-to-hand configuration. A novel Kohonen's self-organizing map (KSOM) based visual servoing scheme has been proposed for a redundant manipulator with 7 degrees of freedom (DOF). The inverse kinematic relationship of the manipulator is learned using a Kohonen's self-organizing map. This learned map is shown to be an approximate estimate of the inverse Jacobian, which can then be used in conjunction with the proportional controller to achieve closed loop servoing in real-time. It is shown through Lyapunov stability analysis that the proposed learning based servoing scheme ensures global stability. A generalized weight update law is proposed for KSOM based inverse kinematic control, to resolve the redundancy during the learning phase. Unlike the existing visual servoing schemes, the proposed KSOM based scheme eliminates the computation of the pseudo-inverse of the Jacobian matrix in real-time. This makes the proposed algorithm computationally more efficient. The proposed scheme has been implemented on a 7 DOF PowerCube(TM) robot manipulator with visual feedback from two cameras.