Decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks

2012 ◽  
Vol 23 (3-4) ◽  
pp. 1051-1060 ◽  
Author(s):  
Shuai Li ◽  
Hongzhu Cui ◽  
Yangming Li ◽  
Bo Liu ◽  
Yuesheng Lou
Keyword(s):  
Neural Networks ◽  
Decentralized Control ◽  
Recurrent Neural Networks ◽  
Redundant Manipulators

Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

2012 ◽  
Vol 91 ◽  
pp. 1-10 ◽  
Author(s):  
Shuai Li ◽  
Sanfeng Chen ◽  
Bo Liu ◽  
Yangming Li ◽  
Yongsheng Liang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Redundant Manipulators ◽  
Kinematic Control

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

Robotica ◽  
2019 ◽  
Vol 38 (8) ◽  
pp. 1495-1512
Author(s):  
Ahmed A. Hassan ◽  
Mohamed El-Habrouk ◽  
Samir Deghedie
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Recurrent Neural Networks ◽  
Inverse Kinematics ◽  
Optimization Problem ◽  
Problem Formulation ◽  
Joint Space ◽  
Problem Solution ◽  
Redundant Manipulators ◽  
Joint Angles

SUMMARYThe Inverse Kinematics (IK) problem of manipulators can be divided into two distinct steps: (1) Problem formulation, where the problem is developed into a form which can then be solved using various methods. (2) Problem solution, where the IK problem is actually solved by producing the values of different joint space variables (joint angles, joint velocities or joint accelerations). The main focus of this paper is concentrated on the discussion of the IK problem of redundant manipulators, formulated as a quadratic programming optimization problem solved by different kinds of recurrent neural networks.

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