%0 Journal Article %1 Shatkay2002 %A SHATKAY, H. %A KAELBLING, L. P. %D 2002 %J Journal of Artificial Intelligence Research %K constraints, geometric hidden markov model. %P 167-207 %T Learning geometrically-constrained hidden Markov models for robot navigation: bridging the topological-geometrical gap %V 16 %X 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.

@article{Shatkay2002, abstract = {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.}, added-at = {2011-08-05T23:54:50.000+0200}, author = {SHATKAY, H. and KAELBLING, L. P.}, biburl = {https://www.bibsonomy.org/bibtex/26356b93ecc2f07f23f253657766369f8/lgmarujo}, file = {:PhD Files\\Modelagem Matematica_PO\\Markov Models\\Shatkay Geom-Constr HMM Robot Nav JAIR 16 2002.pdf:PDF}, interhash = {57a48ea50d36c71a0ba5a64f2b587899}, intrahash = {6356b93ecc2f07f23f253657766369f8}, journal = {Journal of Artificial Intelligence Research}, keywords = {constraints, geometric hidden markov model.}, owner = {Lino}, pages = {167-207}, timestamp = {2011-08-05T23:54:51.000+0200}, title = {Learning geometrically-constrained hidden Markov models for robot navigation: bridging the topological-geometrical gap}, volume = 16, year = 2002 }