ニューラルネットワークによるパラレルマニピュレータの運動学合成
Kinematic Synthesis of Parallel Manipulator via Neural Network Approach
Tricept型パラレルロボットの逆運動学を多層パーセプトロンとRBFネットワークで解き、精度と速度の両面で有効性を示した研究。
著者: J. Ghasemi, R. Moradinezhad, M. A. Hosseini
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
In this research, Artificial Neural Networks (ANNs) have been used as a powerful tool to solve the inverse kinematic equations of a parallel robot. For this purpose, we have developed the kinematic equations of a Tricept parallel kinematic mechanism with two rotational and one translational degrees of freedom (DoF). Using the analytical method, the inverse kinematic equations are solved for specific trajectory, and used as inputs for the applied ANNs. The results of both applied networks (Multi-Layer Perceptron and Redial Basis Function) satisfied the required performance in solving complex inverse kinematics with proper accuracy and speed.
関連論文
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