Grasp planning to maximize task coverage
Grasp planning to maximize task coverage
复制标题
把握规划，最大化任务覆盖率
DOI：
10.1177/0278364915583880
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Yu Sun
中科院分区：
文献类型：
--
作者：
Yun Lin;Yu Sun
本文提出了一种基于任务干扰分布的面向任务的抓取质量度量，可用于搜索覆盖干扰分布最重要部分的抓取。本文没有使用均匀分布的任务扳手空间，而是使用根据任务演示期间捕获的干扰数据构建的非参数统计分布模型来对操作任务进行建模。最大化所提出的抓取质量标准所产生的抓取易于增加最常见干扰的覆盖范围。为了降低高维机器人手配置空间中搜索的计算复杂性，并避免对应问题，候选抓握是根据由一组给定拇指放置和拇指方向限制的简化配置空间来计算的。所提出的方法已经在模拟和真实机器人系统上进行了测试。在模拟中，该方法在多个操作任务中使用 Barrett 手和 Shadow 手进行了验证。在物理机器人平台上的实验验证了所提出的抓取指标和成功率之间的一致性。
This paper proposes a task-oriented grasp quality metric based on distribution of task disturbance, which could be used to search for a grasp that covers the most significant part of the disturbance distribution. Rather than using a uniformly distributed task wrench space, this paper models a manipulation task with a non-parametric statistical distribution model built from the disturbance data captured during the task demonstrations. The grasp resulting from maximizing the proposed grasp quality criterion is prone to increasing the coverage of most frequent disturbances. To reduce the computational complexity of the search in a high-dimensional robotic hand configuration space, as well as to avoid the correspondence problem, the candidate grasps are computed from a reduced configuration space that is confined by a set of given thumb placements and thumb directions. The proposed approach has been tested both in simulation and on a real robotic system. In simulation, the approach was validated with a Barrett hand and a Shadow hand in several manipulation tasks. Experiments on a physical robotic platform verified the consistency between the proposed grasp metric and the success rate.