NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor

IF 3.6 Q2 MANAGEMENT
Abdel-Nasser Sharkawy, Mustafa M. Ali
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引用次数: 3

Abstract

Background: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results: The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions: Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work.
仅使用关节位置传感器的安全人机协作NARX神经网络
背景:安全是人机在同一工作空间或区域进行协作时必须考虑的非常必要的问题。方法:在本文中,开发了一个具有外输入神经网络(NARXNN)的非线性自回归模型,用于检测机械手和人之间的碰撞。NARXNN的设计取决于机械手关节的动力学,并且只考虑机械手关节固有的位置传感器的信号。因此,该网络可以应用于任何传统的机器人。用于训练所设计的NARXNN的数据是通过两个实验生成的,考虑了机械手的正弦关节运动。第一个实验是使用自由接触运动来执行的,而在第二个实验中,由人手与机器人进行随机碰撞。NARXNN的训练过程是使用MATLAB中的Levenberg–Marquardt算法进行的。考虑到与用于训练的数据不同的数据和条件,研究了所开发方法的评估和有效性(%)。实验使用KUKA LWR IV机械手进行。结果:实验结果表明,该训练方法在估计关节外力矩和正确检测碰撞方面是有效的。结论:最后,将所提出的NARXNN与我们之前工作中提出的其他NN架构进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Logistics-Basel
Logistics-Basel Multiple-
CiteScore
6.60
自引率
0.00%
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0
审稿时长
11 weeks
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