Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
International Journal of Systems Science
Computers & Mathematics with Applications
Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Resolve redundancy with constraints for obstacle and singularity avoidance subgoals
International Journal of Robotics and Automation
Robotics and Autonomous Systems
A delayed lagrangian network for solving quadratic programming problems with equality constraints
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators