Pre-Grasp Sliding Manipulation of Thin Objects Using Soft, Compliant, or Underactuated Hands
Pre-Grasp Sliding Manipulation of Thin Objects Using Soft, Compliant, or Underactuated Hands
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DOI：
10.1109/lra.2019.2892591
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发表时间：
2019-04-01
影响因子：
5.2
通讯作者：
Dollar, Aaron M.
中科院分区：
文献类型：
--
作者：
Hang, Kaiyu;Morgan, Andrew S.;Dollar, Aaron M.
我们解决了预抓取滑动操作的问题，当无法从平面直接抓取薄物体时，这是一项基本技能。利用软，符合，或欠驱动的机器人手的被动可重构性，我们制定了这个问题作为一个综合的运动和把握规划问题，并计划直接在机器人配置空间的操作。而不是显式地预先计算一对有效的开始和目标配置，然后在一个单独的步骤规划的路径来连接它们，我们的规划积极的样品开始和目标机器人配置从配置采样区域建模的对象和支持表面的几何形状。当随机连接成对的采样的开始和目标配置时，规划器验证任何连接的对是否可以实现任务以最终确认解决方案。建议的规划器的实施和评估都在模拟和一个真实的机器人。鉴于固有的遵守耶鲁T42的手，我们放宽了运动的限制，并表明规划性能显着提高。此外，我们表明，我们的规划优于两个基线规划，它可以处理对象和支持表面的任意几何形状和大小。
We address the problem of pregrasp sliding manipulation, which is an essential skill when a thin object cannot be directly grasped from a flat surface. Leveraged on the passive reconfigurability of soft, compliant, or underactuated robotic hands, we formulate this problem as an integrated motion and grasp planning problem, and plan the manipulation directly in the robot configuration space. Rather than explicitly precomputing a pair of valid start and goal configurations, and then in a separate step planning a path to connect them, our planner actively samples start and goal robot configurations from configuration sampleable regions modeled from the geometries of the object and support surface. While randomly connecting the sampled start and goal configurations in pairs, the planner verifies whether any connected pair can achieve the task to finally confirm a solution. The proposed planner is implemented and evaluated both in simulation and on a real robot. Given the inherent compliance of the employed Yale T42 hand, we relax the motion constraints and show that the planning performance is significantly boosted. Moreover, we show that our planner outperforms two baseline planners, and that it can deal with objects and support surfaces of arbitrary geometries and sizes.