| Volume 24 Issue 1 - Publication Date: 1 January 2005 |
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| Learning Motion Patterns of
People for Compliant Robot Motion |
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| M. Bennewitz, W.
Burgard Department of Computer Science, University of Freiburg,
79110 Freiburg, Germany, G. Cielniak Department of Technology,
Órebro University, 70182 Órebro, Sweden and S. Thrun
Computer Science Department, Stanford University, Stanford, CA,
USA |
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| 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. In this
paper we propose a technique for learning collections of trajectories
that characterize typical motion patterns of persons. Data recorded with
laser-range finders are clustered using the expectation maximization algorithm.
Based on the result of the clustering process, we derive a hidden Markov
model 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 of a mobile robot. 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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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
1 |
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Example
One: Application of EM: this video shows the evolution of the
model components during an application of EM. (156kb) |
2 |
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Example
Two: Verifying a person's location: in this experiment the robot
updates its belief about the position of a person while it is moving.
(1.2 MB) |
3 |
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Example
Three: Tracking the positions of multiple persons: this video
shows the evolution of the belief about the positions of two persons.
(3.3 MB) |
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