Computing the best grasp in a discrete point set with wrench-oriented grasp quality measures
Computing the best grasp in a discrete point set with wrench-oriented grasp quality measures
复制标题
使用面向扳手的抓握质量测量来计算离散点集中的最佳抓握
DOI：
10.1007/s10514-018-9788-4
复制
发表时间：
2018
影响因子：
3.5
通讯作者：
Yu Zheng
中科院分区：
文献类型：
--
作者：
Yu Zheng
本文提出了一种新的解决方案，计算最佳把握在一个离散点集的性能质量的把握是衡量其能力，适用于扳手的把握对象的问题。首先，它揭示了各种扳手为导向的把握质量的措施，考虑到不同的物理性质的把握，可以写在一个统一的形式作为一个规集在把握扳手集的最大比例因子。此外，它已被推导出的最大比例因子是等于最小值的支持功能的把握扳手设置在所有方向上，可以通过评估的支持功能在一个序列的方向。在此基础上，如果一个新的抓取扳手在序列中任意方向或任意特定方向上的支持函数小于当前最佳抓取的质量值，则可以快速判定该抓取比当前最佳抓取差。这样，就不需要计算新抓取的精确质量值。此外，我们列举候选把握点集中的自适应方式，这样的把握，更有可能超越当前的最佳把握将首先检查，这有助于找到最佳把握更早，并显着减少候选把握的数量进行全面检查。该算法通过快速抓取比较和自适应抓取枚举，在普通PC机上只需几十秒到几个小时就能计算出三维物体上几十到几百个点的最佳抓取，比暴力搜索快两到几个数量级。此外，扳手为导向的把握质量的措施和所提出的算法扩展到真实的场景，涉及机器人的手预测和计算的最佳抓持对象的指尖可达接触点集给定的手位姿。
This paper presents a novel solution to the problem of computing the best grasp in a discrete point set where the performance quality of a grasp is measured by its capability to apply wrenches to the grasped object. First, it is revealed that various wrench-oriented grasp quality measures, considering different physical properties of a grasp, can be written in a unified form as the maximum scale factor of a gauge set in a grasp wrench set. Also, it has been deduced that the maximum scale factor is equal to the minimum value of the support function of the grasp wrench set over all directions and can be computed by evaluating the support function in a sequence of directions. On this basis, we can quickly determine that a new grasp is worse than the current best grasp if the support function of its grasp wrench set in any direction in the sequence or any particular direction is less than the quality value of the current best grasp. In this way, there is no need to calculate the exact quality value of the new grasp. Furthermore, we enumerate candidate grasps in the point set in an adaptive way such that grasps that are more likely to outperform the current best grasp will be checked first, which helps find the best grasp earlier and significantly reduce the number of candidate grasps to be fully examined. With the aid of the quick grasp comparison and the adaptive grasp enumeration, the proposed algorithm takes tens of seconds to several hours on a normal PC to compute the best grasp in tens to hundreds of points on 3-D objects and it is two to several orders of magnitude faster than the brute-force search. Moreover, the wrench-oriented grasp quality measures and the proposed algorithm are extended to the real scenario involving robot hands to predict and compute the best grasps on objects in reachable contact point sets of fingertips by given hand poses.