Locally optimal navigation among movable obstacles in unknown environments
Locally optimal navigation among movable obstacles in unknown environments
复制标题
未知环境中可移动障碍物之间的局部最优导航
DOI：
10.1109/humanoids.2014.7041342
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
H. Christensen
中科院分区：
文献类型：
--
作者：
M. Levihn;Mike Stilman;H. Christensen
移动操纵器和人形机器人应该能够利用其操纵功能将障碍物移开。这个概念被捕获在可移动障碍物（NAMO）之间的导航领域。尽管存在各种NAMO算法，但他们通常会假设全世界知识。相比之下，实际机器人系统仅具有有限的传感器范围和部分环境知识。在这项工作中，我们介绍了第一个NAMO系统，适用于能够处理大量可能的对象运动和任意对象形状的未知环境，同时保证给定知识的最佳决策。我们证明了多达70个障碍的经验结果。
Mobile manipulators and humanoid robots should be able to utilize their manipulation capabilities to move obstacles out of their way. This concept is captured within the domain of Navigation Among Movable Obstacles (NAMO). While a variety of NAMO algorithms exists, they typically assume full world knowledge. In contrast, real robot systems only have limited sensor range and partial environment knowledge. In this work we present the first NAMO system for unknown environments capable of handling a large set of possible object motions and arbitrary object shapes while guaranteeing optimal decision making for the given knowledge. We demonstrate empirical results with up to 70 obstacles.