Planar in-hand manipulation via motion cones

Planar in-hand manipulation via motion cones
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DOI:
10.1177/0278364919880257
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发表时间:
2019-10
期刊:
The International Journal of Robotics Research
影响因子:
--
通讯作者:
Nikhil Chavan-Dafle;Rachel Holladay;Alberto Rodriguez
Nikhil Chavan-Dafle;Rachel Holladay;Alberto Rodriguez
中科院分区:
其他
文献类型:
--
作者:
Nikhil Chavan-Dafle;Rachel Holladay;Alberto Rodriguez

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在本文中,我们介绍了计算在平面上推动的物体的可行运动集的机制和算法。该组称为运动锥体,之前曾针对水平面上的非抓取操作任务进行过描述。我们将其构造推广到更广泛的平面任务,例如包括重力在内的外力影响推动动力学的任务,或抓取任务,其中抓手、物体和推动器之间存在复杂的摩擦相互作用。我们证明运动锥体是由一组低曲率表面定义的,并用多面体锥体对其进行近似。我们通过运动跟踪系统记录的数千次推动实验来验证其有效性。运动锥抽象了摩擦推动动力学中涉及的代数,可用于模拟、规划和控制。在本文中,我们演示了它们在基于采样的规划算法中动态传播步骤的用途。通过限制规划器仅通过运动锥的内部进行探索,我们获得了对系统摩擦参数的有限不确定性具有鲁棒性的操纵策略。我们的规划器生成手动操作轨迹,其中涉及必要时从物体的不同侧面连续推动的序列,速度比同等算法提高 5-1,000 倍。
In this article, we present the mechanics and algorithms to compute the set of feasible motions of an object pushed in a plane. This set is known as the motion cone and was previously described for non-prehensile manipulation tasks in the horizontal plane. We generalize its construction to a broader set of planar tasks, such as those where external forces including gravity influence the dynamics of pushing, or prehensile tasks, where there are complex frictional interactions between the gripper, object, and pusher. We show that the motion cone is defined by a set of low-curvature surfaces and approximate it by a polyhedral cone. We verify its validity with thousands of pushing experiments recorded with a motion tracking system. Motion cones abstract the algebra involved in the dynamics of frictional pushing and can be used for simulation, planning, and control. In this article, we demonstrate their use for the dynamic propagation step in a sampling-based planning algorithm. By constraining the planner to explore only through the interior of motion cones, we obtain manipulation strategies that are robust against bounded uncertainties in the frictional parameters of the system. Our planner generates in-hand manipulation trajectories that involve sequences of continuous pushes, from different sides of the object when necessary, with 5–1,000 times speed improvements to equivalent algorithms.