Hand closure model for planning top grasps with soft robotic hands
Hand closure model for planning top grasps with soft robotic hands
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DOI：
10.1177/0278364920947469
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发表时间：
2020-08-10
影响因子：
9.2
通讯作者：
Prattichizzo, Domenico
中科院分区：
文献类型：
--
作者：
Pozzi, Maria;Marullo, Sara;Prattichizzo, Domenico
抓取动作的自动化是机器人技术中最引人注目的挑战之一。近年来，一个主要的趋势已经引起了机器人抓取界的注意：软操作。在设计本质上柔软的机械手的同时，重要的是要设计出考虑到手的特性，但又足够通用的抓取规划策略，以适用于不同的机器人系统。在本文中，我们研究了如何根据基于模型的方法，使用力量和精确的抓握，用柔软的手进行顶部抓握。所谓的闭合签名（CS）是通过将它们与首选抓取方向相关联来建模软手的闭合运动。这个方向可以对准一个合适的方向上的对象，以实现成功的顶部抓取。cs -alignmentis与最近开发的人工智能驱动的抓取规划器相结合，用于调整刚性抓取器，并用于检索要在物体上执行的最佳抓取的估计。所得到的抓取规划器用两个不同的机械手进行了多次实验测试。他成功地抓住了各种各样形状各异的物体。
Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-calledclosure signature(CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. TheCS-alignmentis here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.