English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Hidden Markov models (HMMs) and partially observable Markov decisionprocesses (POMDPs) provide useful tools for modeling dynamical systems. Theyare particularly useful for representing the topology of environments such asroad networks and office buildings, which are typical for robot navigation andplanning. The work presented here describes a formal framework forincorporating readily available odometric information and geometricalconstraints into both the models and the algorithm that learns them. By takingadvantage of such information, learning HMMs/POMDPs can be made to generatebetter solutions and require fewer iterations, while being robust in the faceof data reduction. Experimental results, obtained from both simulated and realrobot data, demonstrate the effectiveness of the approach.

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