The next frontier: combining information gain and distance cost for decentralized multi-robot exploration

The exploration of unknown environments is an important task for autonomous robots. When multiple robots are able to coordinate themselves to explore different areas of the environment, the exploration efficiency can be greatly improved. In this paper, we present a decentralized approach for multi-robot exploration that leverages the classical frontier based methods. We propose a utility function that takes into consideration the information gain and the distance costs of the frontiers to guide the exploration. Moreover, by exchanging information and merging maps, robots are able to better coordinate and avoid the exploration of redundant areas. Experiments performed with both simulated and real robots demonstrate the effectiveness of this approach.