机器人导航的几何约束隐马尔可夫模型学习：弥合拓扑-几何差距
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
摘要：
隐马尔可夫模型（Hidden Markov Models，简称HMM）和部分可观测马尔可夫决策过程（Partially Observable Markov Decision Process，简称POMDP）为动态系统建模提供了有用的工具。它们对于表示道路网络和办公楼等环境的拓扑结构特别有用，这些环境通常用于机器人导航和规划。这里介绍的工作描述了一个正式的框架，将现成的里程信息和几何约束的模型和算法，学习他们。通过利用这些信息，可以使学习HALGOR/POMDP生成更好的解决方案，并需要更少的迭代，同时在面对数据减少时具有鲁棒性。仿真和真实机器人数据的实验结果表明了该方法的有效性。
英文摘要：
Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.
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