Multi-robot exploration is a field which tackles the challenge of exploring a previously unknown environment with a number of robots. This is especially relevant for search and rescue operations where time is essential. Current state of the art approaches are able to explore a given environment with a large number of robots by assigning them to frontiers. However, this assignment generally favors large frontiers and hence omits potentially valuable medium-sized frontiers. In this paper we showcase a novel multi-robot exploration algorithm, which improves and adapts the existing approaches. Through the addition of information gain based ranking we improve the exploration time for closed urban environments while maintaining similar exploration performance compared to the state-of-the-art for open environments. Accompanying this paper, we further publish our research code in order to lower the barrier to entry for further multi-robot exploration research. We evaluate the performance in three simulated scenarios, two urban and one open scenario, where our algorithm outperforms the state of the art by 5% overall.

翻译：多机器人探索领域致力于解决利用多台机器人探索未知环境的挑战。这对于争分夺秒的搜索与救援任务尤为重要。当前最先进的方法通过将机器人分配至前沿区域，能够利用大量机器人探索给定环境。然而，此类分配通常倾向于大型前沿区域，因而忽略了可能具有价值的中等规模前沿区域。本文提出一种新颖的多机器人探索算法，该算法改进并适配了现有方法。通过引入基于信息增益的排序机制，我们提升了封闭城市环境的探索效率，同时在开放环境中保持了与最先进方法相当的探索性能。为降低多机器人探索研究的准入门槛，本文同步公开了研究代码。我们在三个模拟场景（两个城市场景与一个开放场景）中评估了算法性能，结果表明该算法整体上以5%的优势超越了现有最先进方法。