M. Bennewitz, W. Burgard, G. Cielniak, S. Thurn
Learning Motion Patterns of People for Compliant Robot Motion
International Journal of Robotics Research, 24(1), 2005
Abstract
Whenever people move through their environments they do not move
randomly. Instead, they usually follow specific trajectories or motion
patterns corresponding to their intentions. Knowledge about such patterns
enables a mobile robot to robustly keep track of persons in its environment
and to improve its behavior. This paper proposes a technique for learning
collections of trajectories that characterize typical motion patterns of
persons. Data recorded with laser-range finders is clustered using the
expectation maximization algorithm. Based on the result of the clustering
process we derive a Hidden Markov Model (HMM) that is applied to estimate the
current and future positions of persons based on sensory input. We also
describe how to incorporate the probabilistic belief about the potential
trajectories of persons into the path planning process. We present several
experiments carried out in different environments with a mobile robot equipped
with a laser range scanner and a camera system. The results demonstrate that
our approach can reliably learn motion patterns of persons, can robustly
estimate and predict positions of persons, and can be used to improve the
navigation behavior of a mobile robot.
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Bibtex
@ARTICLE{Bennewitz05IJRR,
AUTHOR = {M. Bennewitz and Wolfram Burgard and G. Cielniak and S. Thrun},
TITLE = {Learning Motion Patterns of People for Compliant Robot Motion},
JOURNAL = {International Journal of Robotics Research},
VOLUME = 24,
NUMBER = 1,
YEAR = 2005
}