Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation
Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation
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DOI：
10.1007/s10514-018-9772-z
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发表时间：
2019-02-01
影响因子：
3.5
通讯作者：
Hermans, Tucker
中科院分区：
文献类型：
--
作者：
Sundaralingam, Balakumar;Hermans, Tucker
本文提出了一种执行抓握操作的新颖方法：参考手掌将物体从初始姿势移动到目标姿势而不中断或进行接触的问题。我们执行抓取操作的方法使用运动学轨迹优化，不需要了解物体的动态属性。我们在 Allegro 机器人手上实现了我们的方法，并对 YCB 数据集中的 10 个对象进行了彻底的实验。所提出的方法足够通用，足以为机器人可以抓取的大多数物体生成运动。实验结果支持其在各种物体形状上应用的可行性。我们通过包括避免碰撞和关节空间平滑度成本来探索我们的方法对附加任务要求的适应性。抓取的物体通过使用带符号的距离成本函数来避免与环境的碰撞。我们通过要求平滑的关节轨迹来减少未建模对象动力学的影响。我们还通过制定对象姿态反馈控制器来补偿轨迹执行期间遇到的错误。
This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on ten objects from the YCB dataset. The proposed method is general enough to generate motions for most objects the robot can grasp. Experimental results support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.