A convolution neural network based semi-parametric dynamic model for industrial robot
A convolution neural network based semi-parametric dynamic model for industrial robot
复制标题
基于卷积神经网络的工业机器人半参数动力学模型
DOI：
10.1177/09544062211039875
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Zhenhua Xiong
中科院分区：
文献类型：
--
作者：
Chungang Zhuang;Yihui Yao;Yichao Shen;Zhenhua Xiong
机器人动力学模型在控制、碰撞检测和运动规划等方面有着广泛的应用。准确的动态模型可以为上述应用提供更好的性能。传统的动力学模型存在摩擦力模型假设复杂、需要附加关节力矩传感器等缺陷。本文仅利用电机编码器信号和电机电流，构建了基于卷积神经网络的半参数动态模型。SPD模型不仅包含物理上可行的参数，而且还通过CNN对动态模型进行补偿。参数和非参数部分构成SPD模型。提出了一种轻量级的CNN，以同时确保准确性和计算效率。为了有效地训练CNN模型，提出了一种数据集生成方法，该方法扩展了激励轨迹并且仅使用连续轨迹来记录数据。基于CNN的SPD模型验证了6自由度实验室开发的工业机器人只有本体感受传感器。与传统的刚体动力学（RBD）模型相比，基于CNN的SPD模型的平均误差减少了9.23%的实验结果。同时，提出的基于CNN的方法实现了更好的性能比其他监督方法。
Robot dynamic model is widely applied to control, collision detection and motion planning. Accurate dynamic model can achieve better performance for the above applications. Traditional dynamic models have several limitations, such as the complex hypotheses for friction model and the requirement of additional joint torque sensors. This article constructs a convolution neural network (CNN) based semi-parametric dynamic (SPD) model by only using the motor encoder signals and motor currents. The SPD model not only contains the physically feasible parameters but also compensates the dynamic model by CNN. The parametric and non-parametric parts constitute the SPD model. A lightweight CNN is proposed to simultaneously ensure the accuracy and computational efficiency. To effectively train the CNN model, a dataset generation method, which expands the excitation trajectory and only uses a continuous trajectory to record data, is proposed. The CNN-based SPD model is verified on a 6-DoF laboratory-developed industrial robot only with the proprioceptive sensors. Compared with the traditional rigid body dynamics (RBD) model, the average error of the CNN-based SPD model is reduced by 9.23% in terms of the experimental results. Meanwhile, the proposed CNN-based method achieves better performance than other supervised methods.
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DOI：
10.1109/tencon.2019.8929622
发表时间：
2019-10
期刊：
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)
影响因子：
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作者：
Rajarshi Mukhopadhyay;Ritartha Chaki;A. Sutradhar;P. Chattopadhyay
通讯作者：
Rajarshi Mukhopadhyay;Ritartha Chaki;A. Sutradhar;P. Chattopadhyay
DOI：
10.7936/k7r78c8n
发表时间：
2012
期刊：
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影响因子：
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作者：
通讯作者：
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影响因子：
4.6
作者：
Shaoju Wu;Wei Zhao;Kianoosh Ghazi;Songbai Ji
通讯作者：
Shaoju Wu;Wei Zhao;Kianoosh Ghazi;Songbai Ji
DOI：
10.1109/iros.2017.8206142
发表时间：
2017-09
期刊：
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
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作者：
C. Gaz;Alessandro De Luca
通讯作者：
C. Gaz;Alessandro De Luca
DOI：
10.1145/3351917.3351940
发表时间：
2019-07
期刊：
Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering
影响因子：
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作者：
Congjun Ma;Haipeng Wang;T. Zhao;S. Dian
通讯作者：
Congjun Ma;Haipeng Wang;T. Zhao;S. Dian