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Fast and resilient manipulation planning for target retrieval in clutter
arXiv - CS - Discrete Mathematics Pub Date : 2020-03-24 , DOI: arxiv-2003.11420
Changjoo Nam, Jinhwi Lee, Sang Hun Cheong, Brian Y. Cho, ChangHwan Kim

This paper presents a task and motion planning (TAMP) framework for a robotic manipulator in order to retrieve a target object from clutter. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target without collisions. For fast completion of object rearrangement, the robot aims to optimize the number of pick-and-place actions which often determines the efficiency of a TAMP framework. We propose a task planner incorporating motion planning to generate executable plans which aims to minimize the number of pick-and-place actions. In addition to fully known and static environments, our method can deal with uncertain and dynamic situations incurred by occluded views. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects).

中文翻译:

用于杂波中目标检索的快速且有弹性的操作规划

本文提出了一种用于机器人操纵器的任务和运动规划 (TAMP) 框架,以便从杂波中检索目标对象。我们考虑高密度密闭空间中的物体配置,因此不存在通往目标的无碰撞路径。机器人必须重新定位一些物体才能在不发生碰撞的情况下检索目标。为了快速完成对象重新排列,机器人旨在优化通常决定 TAMP 框架效率的拾放动作的数量。我们提出了一个任务规划器,结合运动规划来生成可执行的计划,旨在最小化拾取和放置动作的数量。除了完全已知的静态环境外,我们的方法还可以处理由遮挡视图引起的不确定和动态情况。
更新日期:2020-03-26
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