Adaptive Filtering for Robust Proprioceptive Robot Impact Detection Under Model Uncertainties
Adaptive Filtering for Robust Proprioceptive Robot Impact Detection Under Model Uncertainties
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DOI：
10.1109/tmech.2014.2315440
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
M. Makarov;Alex Caldas;M. Grossard;P. Rodríguez-Ayerbe;D. Dumur
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文献类型：
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作者：
M. Makarov;Alex Caldas;M. Grossard;P. Rodríguez-Ayerbe;D. Dumur
在安全的人机物理交互的背景下，本文介绍了一种新的方法来检测柔性关节机器人操作器与环境的动态冲击。我们的目标是检测外部影响施加到机器人只使用本体感受信息具有最大的灵敏度。机器人中的几种基于模型的检测方法是基于估计的和实际施加的扭矩之间的差异，称为残差。这种方法的灵敏度可能受到模型不确定性的限制，模型不确定性来源于实验确定的模型参数上的误差，可能随操作条件而变化，或者使用简化模型，这导致对机器人状态的残余依赖。本文的主要贡献包括一个新的自适应残差评估方法，考虑到这种依赖性，否则可能会导致灵敏度和虚警率之间的权衡。所提出的方法仅使用本体感受电机侧测量，并且不需要任何额外的关节位置传感器或力/扭矩传感器。使用带通滤波和与状态相关的动态阈值的比较，对残留的碰撞的动态影响被隔离。滤波器系数的自适应在线估计避免了对参数模型辨识的大量实验的需要。在CEA可反向驱动的ASSIST机器人手臂上的实验评价说明了检测灵敏度的增强。
In the context of safe human-robot physical interaction, this paper introduces a new method for the detection of dynamic impacts of flexible-joint robot manipulators with their environment. The objective is to detect external impacts applied to the robot using only proprioceptive information with maximal sensitivity. Several model-based detection methods in robotics are based on the difference, called residual, between the estimated and the actual applied torques. Sensitivity of such methods can be limited by model uncertainties that originate either from errors on experimentally identified model parameters, possibly varying with the operating conditions, or the use of simplified models, which results in a residual dependence on the robot's state. The main contribution of this paper consists of a new adaptive residual evaluation method that takes into account this dependence, which otherwise can lead to a tradeoff between sensitivity and false alarm rate. The proposed approach uses only proprioceptive motor-side measurements and does not require any additional joint position sensors or force/torque sensors. Dynamic effects of a collision on the residual are isolated using bandpass filtering and comparison with a state-dependent dynamic threshold. Adaptive online estimation of filter coefficients avoids the need for extensive experiments for parametric model identification. Experimental evaluation on the CEA backdrivable ASSIST robot arm illustrates the enhancement of the detection sensitivity.