Multifingered robotic hands offer stable grasping for a wide variety of objects, yet grasp planning with these hands is more challenging due to the high dimensionality of the search space. In this article, we propose a method for grasping unknown objects from cluttered scenes using a noisy point cloud as an input. Our approach is based on a shape complementarity metric. A fast algorithm for finding a small set of potential grasps is proposed followed by a local shape completion method to infer the occluded parts of the object. Finally, we propose an optimization-based refinement of the hand poses and finger configurations to achieve a power grasp of the target object. The proposed approach is validated extensively both on a simulated and a real world environment. We demonstrate that the proposed grasp planning algorithm produces stable grasps even in heavily dense clutter. Finally, our experiments indicate improved grasp success rate over algorithms that employ precision grasping in the same scene.

中文翻译：

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一种用多指手抓取未知物体的几何方法

多指机械手为各种物体提供稳定的抓取，但由于搜索空间的高维数，用这些手进行抓取规划更具挑战性。在本文中，我们提出了一种使用嘈杂点云作为输入从杂乱场景中抓取未知物体的方法。我们的方法基于形状互补度量。提出了一种用于寻找一小组潜在抓握的快速算法，然后采用局部形状完成方法来推断对象的被遮挡部分。最后，我们提出了一种基于优化的手部姿势和手指配置的细化，以实现对目标对象的强力抓取。所提出的方法在模拟和现实世界环境中都得到了广泛验证。我们证明了所提出的抓取规划算法即使在高度密集的杂波中也能产生稳定的抓取。最后，我们的实验表明，与在同一场景中采用精确抓取的算法相比，抓取成功率有所提高。