基于参数化SOM的机器人轨迹动力学建模

A. C. Padoan, A. Araujo, G. Barreto
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引用次数: 0

摘要

机器人轨迹的规划和控制是一个重要而开放的问题。本文采用无监督神经网络模型构建轨迹的动力学建模。设计了一种具有短期记忆机制的神经网络,当它接收机器人空间位置的当前和过去状态作为输入时,提供相关的关节角度。该模型使用自组织映射(SOM)来近似使用轨迹的某些状态的映射。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic modeling of robotic trajectories using the parametrized SOM
Planning and control of robotic trajectories is an important and open issue. This paper uses an unsupervised neural network model to construct the dynamical modelling of trajectories. A neural network with a short term memory mechanism, was designed to provide the associated joint angles when it receives as input the present and some past states of the robot spatial position. The model uses the self-organizing map (SOM) to approximate the mapping using just some states of the trajectory.
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