Learning motion patterns of people for compliant robot motion

Bennewitz, Maren and Burgard, Wolfram and Cielniak, Grzegorz and Thrun, Sebastian (2005) Learning motion patterns of people for compliant robot motion. The International Journal of Robotics Research, 24 (1). pp. 31-48. ISSN 1741-3176

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Official URL: http://dx.doi.org/10.1177/0278364904048962

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. 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.

Item Type:Article
Additional Information: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.
Keywords:Human robot interaction, Learning activity models, Tracectory clustering, Machine learning, Mobile Robot Navigation., mobile robotics
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
ID Code:1202
Deposited By: Bev Jones
Deposited On:20 Sep 2007
Last Modified:18 Jul 2011 16:17

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