General spatial features for analysis of multi-robot and human activities from raw position data

In this paper, we present a method of quantifying ideas from proxemics into a set of spatial features that can be used for recognizing, classifying, and modeling multi-robot and human activities. We validate the features first with position data from multi-robot activities such as flocking, random walk, and boundary following, and then with human activity data. These features are simple and general, and can also be used as performance measures of robot controllers and in learning robot behaviors.