  If a robot can predict crowds in parts of its environment that are
inaccessible to its sensors, then it can plan to avoid them. This paper
proposes a fast, online algorithm that learns average crowd densities in
different areas. It also describes how these densities can be incorporated into
existing navigation architectures. In simulation across multiple challenging
crowd scenarios, the robot reaches its target faster, travels less, and risks
fewer collisions than if it were to plan with the traditional A* algorithm.
