论文标题
空间感知的重新配置
Space-Aware Reconfiguration
论文作者
论文摘要
我们考虑将一组物理对象重新配置为所需的目标配置的问题,机器人和自动化中的典型(子)任务,在产品组件中出现,包装,存放商店货架等。在本文中,我们解决了一种变体,我们称之为空间感知的重新配置,其目标是最大程度地减少重新配置所需的物理空间,同时遵守对象的无允许碰撞运动。由于对于给定的启动和目标配置,可能不可能重新配置,因此我们将整个目标配置重新转换为一个接收有效的移动序列的位置,在该位置,每个对象依次沿着直线启动到其目标位置,从而使所有中介和目标配置都需要所有中介和目标配置。 我们研究了两个空间感知的重新配置的两种变体,以在平面中经常检查的$ N $单位光盘的设置,具体取决于盘是可区分的(标记)还是无法区分(未标记)。对于标记的情况,我们提出了所有可行初始刚性翻译空间的尺寸$ o(n^4)$的表示,并使用它在$ O(n^6)$ time,最短的有效翻译中找到,或最小化封闭盘或轴与与轴的矩形和目标配置的矩形最小化。对于更难的未标记情况,我们表明,对于几乎每个方向,都有一个方向的翻译,使问题可行。我们用它来设计启发式解决方案,在更严格的可行性概念下,我们优化了翻译。我们提出了这样的启发式方法的实施,该实施在几秒钟内使用数百个光盘解决了未标记的实例。
We consider the problem of reconfiguring a set of physical objects into a desired target configuration, a typical (sub)task in robotics and automation, arising in product assembly, packaging, stocking store shelves, and more. In this paper we address a variant, which we call space-aware reconfiguration, where the goal is to minimize the physical space needed for the reconfiguration, while obeying constraints on the allowable collision-free motions of the objects. Since for given start and target configurations, reconfiguration may be impossible, we translate the entire target configuration rigidly into a location that admits a valid sequence of moves, where each object moves in turn just once, along a straight line, from its starting to its target location, so that the overall physical space required by the start, all intermediate, and target configurations for all the objects is minimized. We investigate two variants of space-aware reconfiguration for the often examined setting of $n$ unit discs in the plane, depending on whether the discs are distinguishable (labeled) or indistinguishable (unlabeled). For the labeled case, we propose a representation of size $O(n^4)$ of the space of all feasible initial rigid translations, and use it to find, in $O(n^6)$ time, a shortest valid translation, or one that minimizes the enclosing disc or axis-aligned rectangle of both the start and target configurations. For the significantly harder unlabeled case, we show that for almost every direction, there exists a translation in this direction that makes the problem feasible. We use this to devise heuristic solutions, where we optimize the translation under stricter notions of feasibility. We present an implementation of such a heuristic, which solves unlabeled instances with hundreds of discs in seconds.