A Novel Distance Cost Approach for Multi-Robot Integrated Exploration

The ability to build reliable maps of unknown environments is important for improving autonomy in robotic systems. Moreover, 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 propose a novel multi-robot Integrated Exploration technique based on distance cost and frontier exploration. We use a utility function that considers both distance cost and coordination of each robot. Our distance cost technique is based on a function that can be adapted for each environment size and the coordination is integrated with the utility function. Experiments performed with simulated robots show that our approach can significantly reduce the exploration time when compared with other distance cost approaches.