Shout and Act (S&A) is an evolution of Bar Systems, a family of algorithms for different classes of complex optimization problems in static and dynamic environments by reactive multi agent systems. We adapt these systems to RoboRescue, where robots explore land looking for victims. When they find someone they “shout” so that robot mates can hear it. The louder the shout, the most important or urgent the finding. Louder shouts can also refer to closeness. Several experiments show that this system works very scalably, and how heterogeneous teams of robots outperform homogeneous ones over a range of task complexity. Finally, our results impact the design of RoboRescue teams: a properly designed combination of robots is cheaper and more scalable when confronted with uncertain maps of victims.