Volume 24 Issue 1 - Publication Date: 1 January 2005
Learning Motion Patterns of People for Compliant Robot Motion
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
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.
Multimedia Key
= Video = Data = Code = Image
Example One: Application of EM: this video shows the evolution of the model components during an application of EM. (156kb)
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)
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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