Publication
Continuous real time POMCP to find-and-follow people by a humanoid service robot
Conference Article
Conference
IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS)
Edition
2014
Pages
741-747
Doc link
http://dx.doi.org/10.1109/HUMANOIDS.2014.7041445
File
Abstract
This 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.
Categories
humanoid robots, planning (artificial intelligence).
Scientific reference
A. Goldhoorn, A. Garrell Zulueta, R. Alquézar Mancho and A. Sanfeliu. Continuous real time POMCP to find-and-follow people by a humanoid service robot, 2014 IEEE-RAS International Conference on Humanoid Robots, 2014, Madrid, Spain, pp. 741-747, IEEE Press.
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