Human-Adaptive Impedance Control Using Recurrent Neural Network for Stability Recovery in Human-Robot Cooperation
Human-Adaptive Impedance Control Using Recurrent Neural Network for Stability Recovery in Human-Robot Cooperation
复制标题
使用循环神经网络进行人体自适应阻抗控制以实现人机合作中的稳定性恢复
DOI：
10.1109/amc44022.2020.9244389
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Ishikawa
中科院分区：
文献类型：
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作者：
Misaki Hanafusa;J. Ishikawa
本文提出了一种人自适应阻抗控制，以实现人机协同操作的一个对象，其中的递归神经网络（RNN）估计的人的状态，用于改善接触稳定性的阻抗控制。这里的人体状态被定义为人体手臂刚度变硬和阻抗控制的稳定性恶化的指示。在所提出的方法中，人的状态估计从整流和积分肌电图（iEMG）信号，而人是操纵与机器人合作的对象。该方法根据人体状态的估计程度，在线调整阻抗参数，使系统更稳定，恢复稳定后，再调整机械参数，使其变轻。通过使用基于阻抗控制的商用机械手与人合作的实验验证了所提出方法的有效性，该机械手可以在作者于2018年7月提出的对象操作期间对净外力做出反应。基于交叉验证的实验结果表明，所提出的RNN可以成功地从iEMG信号中估计人体状态，并检测到人与机器人操纵物体时发生的不期望的振荡。仿真结果表明，所提出的人自适应阻抗控制方法能够根据人的状态在线调整阻抗参数，有效地防止人机协作系统的不稳定。
This paper proposes a human-adaptive impedance control to achieve human-robot co-manipulation of an object, in which a recurrent neural network (RNN) estimates a human state to be used in improving the contact stability of impedance control. The human sate here is defined as what indicates that the human-arm stiffness becomes harder and the stability of the impedance control is deteriorating. In the proposed method, the human state is estimated from rectified-and-integrated electromyogram (iEMG) signals while the person is manipulating the object cooperative with the robot. According to the degree of the estimated human state, the proposed method changes the impedance parameters to be heavier online so as to make the system more stable and then returns the mechanical parameters to be lighter once the stability is restored. The validity of the proposed method is verified by experiments using a commercial-of-the-shelf manipulator that collaborates with a person based on the impedance control, which can react to the net external force during object manipulation proposed by the authors in July 2018. The experimental results based on a cross validation showed that the proposed RNN can successfully estimate the human state from the iEMG signals and detect the undesirable oscillation occurred while the person is manipulating an object with the robot. It has been also confirmed that the proposed human-adaptive impedance control, which adjusts the impedance parameters online according to the human state, is effective to prevent the human-robot cooperative system from coming unstable.