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Efficient Grasp Planning with Reachability Analysis

机译:可达性分析的高效掌握规划

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Grasping can be seen as two steps: placing the hand at a grasping pose and closing the fingers. In this paper, we introduce an efficient algorithm for grasping pose generation. Depend on the hand kinematic, boxes of different sizes are sampled. The reachability for graping is represented by the information, from where the hand can grasp the box firmly. These boxes represent real objects, which at run-time will be decomposed into such boxes, so that the grasping poses for the real object can be generated. Concrete grasps at a grasping pose will be further checked for its grasp quality. Real experiments with two different robotic hands show the efficiency and feasibility of our method.
机译:抓握可以看作是两个步骤:将手放在抓握姿势并关闭手指。在本文中,我们介绍了一种掌握姿势的验证算法。取决于手动运动,采样不同尺寸的盒子。葡萄的可达性由信息表示,从手可以牢牢抓住盒子。这些框表示真实对象,在运行时将被分解成这样的框,以便可以生成掌握对实体的姿势。抓住姿势的混凝土爪将进一步检查其掌握质量。具有两个不同机器人手的真实实验表明了我们方法的效率和可行性。

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