Exploiting Robot Hand Compliance and Environmental Constraints for Edge Grasps.
Exploiting Robot Hand Compliance and Environmental Constraints for Edge Grasps.
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DOI：
10.3389/frobt.2019.00135
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发表时间：
2019
影响因子：
3.4
通讯作者：
Prattichizzo D
中科院分区：
文献类型：
--
作者：
Bimbo J;Turco E;Ghazaei Ardakani M;Pozzi M;Salvietti G;Bo V;Malvezzi M;Prattichizzo D
本文提出了一种方法，可以使用柔软的，无人驾驶的手来掌握无法直接从桌子中挑选的对象。通过将对象拖动到表的边缘，并从突出的部分抓住它，从而执行所谓的幻灯片到边缘的grasps来实现这些抓。这种类型的方法使用环境来促进掌握，被命名为环境约束剥削（ECE），并已被证明可以提高抓地力的稳健性，同时减少计划工作。本文提出了两种策略，即连续滑动和掌握，枢轴和reasp，它们旨在处理不同的物体。在第一个策略中，将手放在对象上，并假定在滑动过程中粘在它上，直到边缘缠绕在物体上并捡起它。相反，在第二个策略中，滑动运动是使用枢轴进行的，因此允许对象相对于将其拖动向边缘的手旋转。然后，一旦物体到达所需的位置，手就会从物体脱离并从侧面移动对象。在这两种策略中，都使用最近提出的软手签名的功能模型来实现用于抓住对象的手的定位，而桌子上的滑动运动是通过使用混合力效率控制器执行的。我们使用与协作机器人组的软手进行了320次握把试验，并使用16个不同的对象进行了试验。实验表明，连续的幻灯片和掌握更适合小物体（例如，信用卡），而枢轴和Re-rasp则在较大的物体（例如，大书）中表现更好。收集的数据用于训练根据对象大小和重量选择最合适的使用策略的分类器。用软手实施ECE策略是迈向现实情况下使用的第一步，在现实世界中，环境应该比在障碍中更多地视为帮助。
This paper presents a method to grasp objects that cannot be picked directly from a table, using a soft, underactuated hand. These grasps are achieved by dragging the object to the edge of a table, and grasping it from the protruding part, performing so-called slide-to-edge grasps. This type of approach, which uses the environment to facilitate the grasp, is named Environmental Constraint Exploitation (ECE), and has been shown to improve the robustness of grasps while reducing the planning effort. The paper proposes two strategies, namely Continuous Slide and Grasp and Pivot and Re-Grasp, that are designed to deal with different objects. In the first strategy, the hand is positioned over the object and assumed to stick to it during the sliding until the edge, where the fingers wrap around the object and pick it up. In the second strategy, instead, the sliding motion is performed using pivoting, and thus the object is allowed to rotate with respect to the hand that drags it toward the edge. Then, as soon as the object reaches the desired position, the hand detaches from the object and moves to grasp the object from the side. In both strategies, the hand positioning for grasping the object is implemented using a recently proposed functional model for soft hands, the closure signature, whereas the sliding motion on the table is executed by using a hybrid force-velocity controller. We conducted 320 grasping trials with 16 different objects using a soft hand attached to a collaborative robot arm. Experiments showed that the Continuous Slide and Grasp is more suitable for small objects (e.g., a credit card), whereas the Pivot and Re-Grasp performs better with larger objects (e.g., a big book). The gathered data were used to train a classifier that selects the most suitable strategy to use, according to the object size and weight. Implementing ECE strategies with soft hands is a first step toward their use in real-world scenarios, where the environment should be seen more as a help than as a hindrance.
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