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Título

Continuous real time POMCP to find-and-follow people by a humanoid service robot

AutorGoldhoorn, Alex ; Garrell, Anaís ; Alquézar Mancho, Renato ; Sanfeliu, Alberto
Fecha de publicación2014
EditorInstitute of Electrical and Electronics Engineers
Citación14th IEEE-RAS International Conference on Humanoid Robots (Humanoids): 741-747 (2014)
ResumenThis study describes and evaluates two new methods for finding and following people in urban settings using a humanoid service robot: the Continuous Real-time POMCP method, and its improved extension called Adaptive Highest Belief Continuous Real-time POMCP follower. They are able to run in real-time, in large continuous environments. These methods make use of the online search algorithm Partially Observable Monte-Carlo Planning (POMCP), which in contrast to other previous approaches, can plan under uncertainty on large state spaces. We compare our new methods with a heuristic person follower and demonstrate that they obtain better results by testing them extensively in both simulated and real-life experiments. More than two hours, over 3 km, of autonomous navigation during real-life experiments have been done with a mobile humanoid robot in urban environments.
DescripciónTrabajo presentado al 14th IEEE-RAS International Conference on Humanoid Robots: Humanoids 2014 "Humans and Robots Face-to-Face", celebrado en Madrid (España) del 18 al 20 de noviembre de 2014.
Versión del editorhttp://dx.doi.org/10.1109/HUMANOIDS.2014.7041445
URIhttp://hdl.handle.net/10261/127332
DOI10.1109/HUMANOIDS.2014.7041445
Aparece en las colecciones: (IRII) Comunicaciones congresos
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