用户引导的数字手抓取规划

Yi Li, Niclas Delfs, J. Carlson
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引用次数: 0

摘要

为了组装一个部件(例如,发动机),人手必须通过在多个接触点施加力和扭矩来完全控制其运动。今天,使用数字人体建模(DHM)工具合成一个零件的良好手抓通常是耗时的,因为这些工具需要用户详细的手动输入,例如手动将数字手放在可行的抓握位置周围,然后关闭零件周围的手指。在之前的一篇论文中,我们提出了两种不同的方法(即点距最短距离和环境间隙),通过考虑环境距离约束来为零件表面上色,以便装配仿真专家等用户可以轻松识别可行的抓取位置。由于实现的鲁棒性,即使是带有裂纹和间隙等常见几何缺陷的三角形网格也可以处理。在本文中,我们利用这种可行性分析,并提出了一种用户指导的抓取规划方法,该方法大大加快了抓取建模过程。首先,用户选择一个预定义的握把类型,然后为手设置一个接近方向。为了综合许多抓握,我们随机采样手在接近方向的旋转。接下来,手向零件移动,直到手的抓取中心点(GCP)达到零件的几何形状或检测到手与零件之间的碰撞。如果检测到碰撞,我们向后移动手,直到手和部件之间不再发生碰撞。最后,我们将手的手指合在一起来合成一个抓握。这样,我们可以快速综合多种抓取方式,让用户在抓取质量最好的抓取方式中进行选择,其中每个抓取质量都使用相应的6D抓取扳手外壳进行计算。我们相信,当涉及到用户可用性时,这种以用户为导向的抓取规划方法可以显著增强DHM工具,如智能移动人体模型(IMMA)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User-guided grasp planning for digital hand
In order to assemble a part (of e.g., an engine), a human hand must obtain complete control of its motion through application of forces and toques at multiple contact points. Today, it is often time-consuming to synthesize a good hand grasp of a part using Digital Human Modeling (DHM) tools because these tools require detailed manual inputs from a user such as manually placing a digital hand around a feasible grasp location and then closing the fingers around the part. In a previous paper, we presented two different methods (i.e., Pointwise Shortest Distance and Environment Clearance) to color part surfaces by taking environmental distance constraints into account so that a user such as an assembly simulation expert can easily identify feasible grasp locations. Due to the robustness of the implementation, even triangle meshes with common geometric flaws such as cracks and gaps can be handled. In this paper, we leverage on this feasibility analysis and present a user-guided grasp planning approach that significantly speeds up the grasp modeling process. First, the user selects a predefined grip type and then sets an approach direction for the hand. To synthesize many grasps, we randomly sample the hand’s rotation around the approach direction. Next, the hand is moved towards the part until the hand’s Grasp Center Point (GCP) reaches the geometry of the part or a collision between the hand and the part is detected. If a collision was detected, we move the hand backwards until there is no collision between the hand and the part anymore. Finally, we close the hand’s fingers around the part to synthesize a grasp. In this way, we can quickly synthesize a multitude of grasps and let the user choose among the ones with the best grasp qualities, where each grasp quality is computed using the corresponding 6D grasp wrench hull. We believe that this user-guided grasp planning approach can significantly enhance DHM tools such as Intelligently Moving Manikins (IMMA) when it comes to user usability.
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