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Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach

机译:杂乱表面上多个可移动物体的几何重排:混合推理方法

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We introduce a novel computational method for geometric rearrangement of multiple movable objects on a cluttered surface, where objects can change locations more than once by pick and/or push actions. This method consists of four stages: (i) finding tentative collision-free final configurations for all objects (all the new objects together with all other objects in the clutter) while also trying to minimize the number of object relocations, (ii) gridization of the continuous plane for a discrete placement of the initial configurations and the tentative final configurations of objects on the cluttered surface, (iii) finding a sequence of feasible pick and push actions to achieve the final discrete placement for the objects in the clutter from their initial discrete place, while simultaneously minimizing the number of object relocations, and (iv) finding feasible final configurations for all objects according to the optimal task plan calculated in stage (iii). For (i) and (iv), we introduce algorithms that utilize local search with random restarts; for (ii), we introduce a mathematical modeling of the discretization problem and use the state-of-the-art ASP reasoners to solve it; for (iii) we introduce a formal hybrid reasoning framework that allows embedding of geometric reasoning in task planning, and use the expressive formalisms and reasoners of ASP. We illustrate the usefulness of our integrated AI approach with several scenarios that cannot be solved by the existing approaches. We also provide a dynamic simulation for one of the scenarios, as supplementary material.
机译:我们介绍了一种新的计算方法,用于在杂乱的表面上的多个可移动物体的几何重新排列,其中物体可以通过拾取和/或推动动作更加多次改变一次。此方法由四个阶段组成:(i)为所有对象(所有新对象以及杂波中的所有其他对象一起设置的暂定的碰撞最终配置,同时还尝试最小化对象重定位的数量,(ii)的网格用于离散放置初始配置的连续平面和杂乱表面上的对象的暂定最终配置,(iii)找到一种可行的选择序列和推动动作,以实现杂乱中的对象的最终离散放置离散的位置,同时最小化对象重定位的数量,并且(iv)根据在阶段(iii)中计算的最佳任务计划来找到所有对象的可行的最终配置。对于(i)和(iv),我们介绍了使用随机重启的本地搜索的算法;对于(ii),我们介绍了离散化问题的数学建模,并利用最先进的ASP推理员解决;对于(iii),我们介绍了一个正式的混合推理框架,允许在任务规划中嵌入几何推理,并使用asp的表现形式和推理员。我们说明了我们的集成AI方法的有用性,并具有现有方法无法解决的几种情况。我们还为其中一个方案提供动态模拟,作为补充材料。

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