缆索驱动机器人挠度图的人工神经网络预测

L. Notash
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引用次数: 4

摘要

本文应用人工神经网络建立了缆索驱动机器人的学习模型。对于已知的输入输出数据和已知的关系(回归问题),利用多层人工神经网络预测缆索驱动并联机器人的挠度图。研究了平面机器人和平移空间机器人两种缆索机器人的模型。利用人工神经网络和非线性优化方法生成了电缆机器人的挠度图。在整个相关的离散化工作空间中,使用更少的姿态进行训练的人工神经网络模型的预测偏差非常令人满意,并且与非线性优化方法获得的结果相当。此外,人工神经网络模型可以预测姿态的偏转,这是非线性优化方法无法预测的。此外,随着机器人/任务参数(如有效载荷)的变化,人工神经网络模型可以预测准确的偏转。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Network Prediction of Deflection Maps for Cable-Driven Robots
In this paper, the learning models of cable-driven robots are developed applying the artificial neural network (ANN). For known input and output data and known relationships (regression problem), the deflection maps of cable-driven parallel robots are predicted utilizing a multi-layer ANN. Two cable robots, a planar robot and a translational spatial robot, are examined to evaluate their models. The deflection maps of these cable robots are generated using the ANN and a non-linear optimization method. The predicted deflections of the ANN models, using much less number of poses for training, are highly satisfactory and comparable to the results obtained by a nonlinear optimization method throughout the pertinent discretized workspaces. In addition, ANN models could predict the deflections for poses that the nonlinear optimization methods may not. Moreover, with variations in robot/task parameters, such as payload, ANN models may predict accurate deflections.
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