Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Robot Dynamics and Control
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators
Journal of Intelligent and Robotic Systems
Visual–Motor Coordination Using a Quantum Clustering Based Neural Control Scheme
Neural Processing Letters
Image moments: a general and useful set of features for visual servoing
IEEE Transactions on Robotics
A dual neural network for kinematic control of redundant robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kinematic control of redundant robots and the motion optimizabilitymeasure
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Indirect Iterative Learning Control for a Discrete Visual Servo Without a Camera-Robot Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Speeding up the learning of robot kinematics through function decomposition
IEEE Transactions on Neural Networks
Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map
Robotics and Autonomous Systems
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
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This paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.