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Automatic 3-D Manipulation of Soft Objects by Robotic Arms With an Adaptive Deformation Model

机译:具有自适应变形模型的机械臂对软物体的自动3-D操纵

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In this paper, we present a new feedback method to automatically servo-control the 3-D shape of soft objects with robotic manipulators. The soft object manipulation problem has recently received a great deal of attention from robotics researchers because of its potential applications in, e.g., food industry, home robots, medical robotics, and manufacturing. A major complication to automatically control the shape of an object is the estimation of its deformation properties, which determines how the manipulator's motion actively transforms into deformations. Note that these properties are rarely known beforehand, and its offline parametric identification is difficult and/or impractical to conduct in many applications. To cope with this issue, we developed a new algorithm that computes in real time the unknown deformation parameters of a soft object; this algorithm provides a valuable adaptive behavior to the deformation controller, something we cannot achieve with traditional fixed-model approaches. In contrast with most controllers in the literature, our new method can explicitly servo-control 3-D deformations (and not just 2-D image projections) in an entirely model-free way. To validate the proposed adaptive controller, we present a detailed experimental study with robotic manipulators.
机译:在本文中,我们提出了一种新的反馈方法,该方法可以通过机器人操纵器自动伺服控制3D形状的软物体。由于其在例如食品工业,家用机器人,医疗机器人和制造业中的潜在应用,软对象操纵问题近来受到机器人研究者的广泛关注。自动控制对象形状的主要复杂之处在于其变形属性的估计,这决定了操纵器的运动如何主动地转化为变形。请注意,这些属性事先很少为人所知,因此在许多应用程序中进行离线参数识别很困难和/或不切实际。为了解决这个问题,我们开发了一种新算法,可以实时计算软物体的未知变形参数。该算法为变形控制器提供了有价值的自适应行为,这是传统固定模型方法无法实现的。与文献中的大多数控制器相比,我们的新方法可以以完全无模型的方式显式地伺服控制3D变形(而不仅仅是2D图像投影)。为了验证所提出的自适应控制器,我们提出了机器人操纵器的详细实验研究。

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