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This article investigates the effect of incorporating knowledge about the communication possibilities in an exploration algorithm used to map an unknown environment. The mission is to explore a hypothetical disaster site with a small team of robots. The challenge faced by the robot team is to coordinate their actions such that they efficiently explore the environment in their search for victims. The coordination can only be optimal when the robots share the same map. With a limited communication range the map cannot be shared in all circumstances. This article concentrates on the effect of a distributed map, where each robot has only has knowledge of a part of the global map and has no guaranteed connection to the other robot or the operator.

This article investigates the scenario where a small team of robots needs to explore a hypothetical disaster site. The challenge faced by the robot-team is to coordinate their actions such that they efficiently explore the environment in their search for victims. A popular paradigm for the exploration problem is based on the notion of frontiers: the boundaries of the current map from where robots can enter yet unexplored area. Coordinating multiple robots is then about intelligently assigning frontiers to robots. Typically, the assignment of a particular frontier to a particular robot is governed by a cost measure, e.g. the movement costs for the robot to reach the frontier. In more recent approaches these costs are traded off with the potential gain in information if the frontier would be explored by the robot. In this paper we will further investigate the effect of balancing movement costs with information gains while assigning frontiers to robots. In our experiments we will illustrate how various choices for this balance can have a significant impact on the exploratory behavior exposed by the robot team.