Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network |
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Authors: | Indrani Kar Laxmidhar Behera |
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Affiliation: | 1.Indian Institute of Technology,Guwahati,India;2.University of Ulster,Ulster,UK;3.Indian Institute of Technology,Kanpur,India |
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Abstract: | A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF)
robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly
nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors.
In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations
and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse
kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to
make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine
movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator
position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning
scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse
kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time
on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings. |
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