Takayuki Kanda
Dylan F. Glas
Masahiro Shiomi
ABSTRACT
For a robot providing services to people in a public space such as a train station or a shopping mall, it is important to distinguish potential customers, such as window-shoppers, from other people, such as busy commuters. In this paper, we present a series of techniques for anticipating people's behavior in a public space, mainly based on the analysis of accumulated trajectories, and we demonstrate the use of these techniques in a social robot. We placed a ubiquitous sensor network consisting of six laser range finders in a shopping arcade. The system tracks people's positions as well as their local behaviors such as fast walking, idle walking, or stopping. We accumulated people's trajectories for a week, applying a clustering technique to the accumulated trajectories to extract information about the use of space and people's typical global behaviors. This information enables the robot to target its services to people who are walking idly or stopping. The robot anticipates both the areas in which people are likely to perform these behaviors, and also the probable local behaviors of individuals a few seconds in the future. In a field experiment we demonstrate that this system enables the robot to serve people efficiently.
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Index Terms
- Who will be the customer?: a social robot that anticipates people's behavior from their trajectories
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Reviews
James H. Bradford
As the use of ambulatory robots becomes more common in society, a new subdiscipline of human-computer interaction (HCI) is emerging. In the coming years, it will be necessary to operate robots in a way that seems natural to humans. This paper describes an innovative new approach to operating robots that interact with human customers in a shopping area. Human browsing activity was monitored through a series of strategically located laser rangefinders. Then, a computer program predicted individual human trajectories by using recent movement as input to a sophisticated statistical model. When the model predicted browsing behavior, a mobile robot would roll up to the browsing shopper and engage him or her in conversation. Prediction is important in this application because it takes time for the robot to maneuver itself toward specific individuals. The authors conducted a field study in a busy shopping area near an amusement park. This was a complex environment, with many pedestrians moving through it during busy times. Nevertheless, the authors reported a high level of success in identifying probable browsers in the moving crowd. This paper will certainly be of interest to researchers and developers who focus on the creation of intelligent machines intended for human environments. Online Computing Reviews Service
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