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Bimanual grasp planning

机译:双手把握计划

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摘要

The ability to grasp large objects with both hands enables bimanual robot systems to fully employ their capabilities in human-centered environments. Hence, algorithms are needed to precompute bimanual grasping configurations that can be used online to efficiently create whole body grasps. In this work we present a bimanual grasp planner that can be used to build a set of grasps together with manipulability information for a given object. For efficient grasp planning precomputed reachability information and a beneficial object representation, based on medial axis descriptions, are used. Since bimanual grasps may suffer from low manipulability, caused by a closed kinematic chain, we show how the manipulability of a bimanual grasp can be used as a quality measure. Therefore, manipulability clusters are introduced as an efficient way to approximatively describe the manipulability of a given bimanual grasp. The proposed approach is evaluated with a reference implementation, based on Simox [1], for the humanoid robot ARMAR-III [2]. Since the presented algorithms are robot-independent, there are no limitations for using this planner on other robot systems.
机译:双手抓住大物体的能力使双手机器人系统可以在以人为中心的环境中充分利用其功能。因此,需要算法来预先计算可在线使用的双手抓握配置,以有效地创建全身抓握。在这项工作中,我们提供了一个双向抓取计划器,该计划器可用于为给定对象构建一组抓取以及可操作性信息。为了有效地掌握计划,使用了基于中间轴描述的预先计算的可达性信息和有益的对象表示形式。由于由闭合运动链引起的双向抓握可能会受到较低的可操纵性的困扰,因此我们展示了如何将双向抓握的可操纵性用作质量度量。因此,可操作性群集是作为一种有效方法来近似描述给定的双手抓握性的可操作性。针对人形机器人ARMAR-III [2],基于Simox [1]通过参考实现对提出的方法进行了评估。由于提出的算法与机器人无关,因此在其他机器人系统上使用此计划器没有任何限制。

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