Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap

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 o ce 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 e ectiveness of the approach.

• Type: Journal
• Year: 2002
• Where: JAIR
• Authors: Hagit Shatkay, Leslie Pack Kaelbling
• Keywords: Available Odometric Information, Hidden Markov Models, JAIR 2002, Markov Decision