Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
The International Journal of Robotics Research
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (8)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Bennewitz, M.
Right arrow Articles by Thrun, S.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Learning Motion Patterns of People for Compliant Robot Motion

Maren Bennewitz

Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany

Wolfram Burgard

Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany

Grzegorz Cielniak

Department of Technology, Örebro University, 70182 Örebro, Sweden

Sebastian 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.

Key Words: learning activity models • trajectory clustering • machine learning • mobile robot navigation • human robot interaction

The International Journal of Robotics Research, Vol. 24, No. 1, 31-48 (2005)
DOI: 10.1177/0278364904048962
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 The International Journal of Robotics ResearchHome page
Shandong Wu and Y.F. Li
On Signature Invariants for Effective Motion Trajectory Recognition
The International Journal of Robotics Research, August 1, 2008; 27(8): 895 - 917.
[Abstract] [PDF]

Home page
 The International Journal of Robotics ResearchHome page
D. Kulic, W. Takano, and Y. Nakamura
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains
The International Journal of Robotics Research, July 1, 2008; 27(7): 761 - 784.
[Abstract] [PDF]