User-robot collaborative excitation for PAM model identification in exoskeleton robots
User-robot collaborative excitation for PAM model identification in exoskeleton robots
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DOI：
10.1109/iros.2017.8206145
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Masashi Hamaya;Takamitsu Matsubara;T. Noda;T. Teramae;J. Morimoto
中科院分区：
文献类型：
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作者：
Masashi Hamaya;Takamitsu Matsubara;T. Noda;T. Teramae;J. Morimoto
气动人工肌肉（PAM）驱动器已被用作外骨骼，因为他们的继承性的顺应性和高功率重量比。然而，创建准确的模型仍然很困难，主要是由于合规性问题;模型可以通过用户施加的力来改变。因此，用户和机器人动作都需要被考虑用于装备在外骨骼机器人中的PAM的充分激励，这与只能由机器人动作充分激励的典型刚性致动器不同。在本文中，我们提出了一个用户-机器人协同激励方法PAM模型识别作为一个主动学习框架，通过推导和执行最佳的用户和机器人的行动，在每一步高斯过程顺序收集数据。由机器人执行的最佳动作显示在监视器上，使用户能够执行它们。我们进行了实验，使用动力肘外骨骼与PAM致动器。实验结果表明，我们的方法可以更有效地识别PAM模型比标准的模型识别方法，不使用任何数据，通过用户-机器人协作。
Pneumatic Artificial Muscle (PAM) actuators have been used as exoskeletons because of their inherited compliance and high power-weight ratio. However, creating accurate models remains difficult mainly due to the compliance issue; the model can be changed by the force applied by the user. Therefore, both user and robot actions need to be considered for sufficient excitation of PAMs that are equipped in exoskeleton robots, unlike typical rigid actuators that can only be sufficiently excited by robot actions. In this paper, we propose a user-robot collaborative excitation approach for PAM model identification as an active learning framework for sequentially collecting data by deriving and executing optimal user and robot actions at each step with Gaussian processes. The optimal actions, which are executed by the robot, are displayed on a monitor that enables the user to execute them. We conducted experiments using a powered elbow exoskeleton with a PAM actuator. Experimental results show that our method can more efficiently identify the PAM model than a standard model identification method that does not use any data acquired through user-robot collaboration.