论文标题
可见性障碍之间的可见性导航
Visibility-Aware Navigation Among Movable Obstacles
论文作者
论文摘要
在本文中，我们研究了可见性的机器人在可移动障碍物（VANAMO）之间导航的问题。 Vanamo是著名的NAMO机器人计划问题的一种变体，对机器人运动和对象移动性更加可见性约束。这个新问题提出了一个限制性假设，即地图是完全可见的，并且对象位置是完全已知的。我们提供了VANAMO问题的正式定义，并提出了用于解决此类问题的外观和操纵后排（LAMB）算法。 LAMB具有简单的基于视觉的API，它使其更容易转移到现实世界的机器人应用程序和尺度上，以转移到大型3D环境中。为了评估羔羊，我们构建了一组任务，这些任务说明了在未知环境中移动基础操纵问题中可能出现的可见性和对象移动性之间的复杂相互作用。我们表明，羔羊表现优于NAMO和可见性运动计划方法，以及对复杂的操纵问题和部分可观察性的简单组合。
In this paper, we examine the problem of visibility-aware robot navigation among movable obstacles (VANAMO). A variant of the well-known NAMO robotic planning problem, VANAMO puts additional visibility constraints on robot motion and object movability. This new problem formulation lifts the restrictive assumption that the map is fully visible and the object positions are fully known. We provide a formal definition of the VANAMO problem and propose the Look and Manipulate Backchaining (LaMB) algorithm for solving such problems. LaMB has a simple vision-based API that makes it more easily transferable to real-world robot applications and scales to the large 3D environments. To evaluate LaMB, we construct a set of tasks that illustrate the complex interplay between visibility and object movability that can arise in mobile base manipulation problems in unknown environments. We show that LaMB outperforms NAMO and visibility-aware motion planning approaches as well as simple combinations of them on complex manipulation problems with partial observability.
