Who will be the customer?

摘要

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.