Modeling of a Five-Link Biped Robot Dynamics Using Neural Networks

Bigdeli, Nooshin; Afshar, Keyvan Karim; Lame, B. I.; Zohrabi, Abtin

doi:10.3923/jas.2008.3612.3620

Cited by 5 publications

(1 citation statement)

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“…The characteristics of the considered robot are demonstrated in Table 2. Furthermore, it has been shown in several works that a neural network with one or two hidden layers is sufficient to approximate any nonlinear function [26][27][28][29]. For the sake of simplicity, one hidden layer for a single-section continuum robot and two hidden layers for two section continuum robot are used as well as a minimum number of neurons that gives good learning and that reflects back on the robot's accuracy during trajectory tracking.…”

Section: Outline and Simulationmentioning

confidence: 99%

Archive of Mechanical Engineering

Ghoul,

Kara,

Djeffal

et al. 2022

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In this paper, neural networks are presented to solve the inverse kinematic models of continuum robots. Firstly, the forward kinematic models are calculated for variable curvature continuum robots. Then, the forward kinematic models are implemented in the neural networks which present the position of the continuum robot's end effector. After that, the inverse kinematic models are solved through neural networks without setting up any constraints. In the same context, to validate the utility of the developed neural networks, various types of trajectories are proposed to be followed by continuum robots. It is found that the developed neural networks are powerful tool to deal with the high complexity of the non-linear equations, in particular when it comes to solving the inverse kinematics model of variable curvature continuum robots. To have a closer look at the efficiency of the developed neural network models during the follow up of the proposed trajectories, 3D simulation examples through Matlab have been carried out with different configurations. It is noteworthy to say that the developed models are a needed tool for real time application since it does not depend on the complexity of the continuum robots' inverse kinematic models.

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Section: Outline and Simulationmentioning

confidence: 99%