Deep-learning damped least squares method for inverse kinematics of redundant robots-Reference-Cited by-同舟云学术

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Deep-learning damped least squares method for inverse kinematics of redundant robots

Published:2021-02 Issue: Volume:171 Page:108821
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Wang Xiaoqi,Liu Xing,Chen Lerui,Hu Heyu

Funder

Shaanxi Science and Technology Association

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference37 articles.

1. Resolved motion rate control of manipulators and human prostheses;Whitney;IEEE Trans. Man-machine Syst.,1969

2. Design of inverse kinematics algorithms: extended Jacobian approximation of the dynamically consistent Jacobian inverse;Ratajczak;Arch. Control Sci.,2015

3. A Technique based on adaptive extended jacobians for improving the robustness of the inverse numerical kinematics of redundant robots;Simas;J. Mech. Robotics,2019

4. Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods;Wampler;IEEE Trans. Syst. Man, Cybernetics,1986

5. Task-priority based redundancy control of robot manipulators;Nakamura;Int. J. Robotics Res.,1987

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