Playing with several roadmaps to solve manipulation problems
Playing with several roadmaps to solve manipulation problems
复制标题
使用多个路线图来解决操作问题
DOI：
10.1109/irds.2002.1041612
复制
发表时间：
2002
期刊：
影响因子：
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通讯作者：
T. Siméon
中科院分区：
文献类型：
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作者：
F. Gravot;R. Alami;T. Siméon
在本文中，我们提出了一种解决方案，旨在解决一大类操作规划问题。这项工作是对我们在开发操纵规划算法方面所做努力的补充。事实上，我们相信，由于引入了符号推理级别，将可以获得更高水平的问题复杂性，特别是涉及多个机器人和多个对象的问题。该解决方案依赖于概率路线图方法(PRM)和自适应地控制多个路线图的构建和扩展的推理级别。我们认为，这一象征性水平是朝着在更好的条件下整合任务规划和几何规划的系统方法迈出的一步，而不是通过粗略的、某种程度上的人工等级分解。本文描述了拟议框架的主要组成部分，以及它的初步成果。
We propose in this paper a resolution scheme that is aimed to be relevant for a large class of manipulation planning problems. This endeavor complements our efforts in developing manipulation planning algorithms. Indeed, we are convinced that a higher level of problems complexity, and particularly those involving multiple robots and multiple objects, will be accessible thanks to the introduction of a symbolic reasoning level. The resolution scheme relies on probabilistic roadmap methods (PRMs) and on a reasoning level that adaptively controls the construction and extension of a number of roadmaps. We consider this symbolic level as a step towards a systematic approach to integrate task planning and geometric planning in better conditions than through a gross, and somewhat, artificial hierarchical decomposition. This paper describes the main ingredients of the proposed framework, and its first results.