Human-friendly robot navigation in dynamic environments

Abstract

The vision-based mechanisms that pedestrians in social groups use to navigate in dynamic environments, avoiding obstacles and each others, have been subject to a large amount of research in social anthropology and biological sciences. We build on recent results in these fields to develop a novel fully-distributed algorithm for robot local navigation, which implements the same heuristics for mutual avoidance adopted by humans. The resulting trajectories are human-friendly, because they can intuitively be predicted and interpreted by humans, making the algorithm suitable for the use on robots sharing navigation spaces with humans. The algorithm is computationally light and simple to implement. We study its efficiency and safety in presence of sensing uncertainty, and demonstrate its implementation on real robots. Through extensive quantitative simulations we explore various parameters of the system and demonstrate its good properties in scenarios of different complexity. When the algorithm is implemented on robot swarms, we could observe emergent collective behaviors similar to those observed in human crowds.