A Deep Learning Approach to Navigating the Joint Solution Space of Redundant Inverse Kinematics and Its Applications to Numerical IK Computations

As an increasing number of robotic manipulators possess seven or more degrees-of-freedom (DoF), solving inverse kinematic (IK) for kinematically redundant manipulators is becoming critical. Numerical optimizations are commonly used to solve the problem due to their generality and accuracy. Unfortuna...

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Veröffentlicht in:	IEEE access Jg. 11; S. 2274 - 2290
Hauptverfasser:	Ho, Chi-Kai, Chan, Li-Wei, King, Chung-Ta, Yen, Ting-Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Deep learning Degrees of freedom End effectors Feature encoding Inverse kinematics Iterative methods Kinematics Manipulators Neural networks Numerical methods Numerical models Obstacle avoidance Optimization Redundancy redundant robotic manipulators Robot arms Robots Solution space Unsupervised learning
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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