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：多机器人探索是一个解决如何利用多个机器人在未知环境中进行探索的领域。这对于时间至关重要的搜索与救援任务尤为关键。当前最先进的方法通过将机器人分配到前沿区域，能够实现大量机器人在给定环境中的探索。然而，这种分配通常倾向于优先选择大规模前沿区域，从而忽略了潜在有价值的中等规模前沿区域。本文提出了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，我们缩短了封闭城市环境中的探索时间，同时在开放环境中保持了与现有技术相当的探索性能。本文还公开了研究代码，以降低后续多机器人探索研究的准入门槛。我们在三个模拟场景（两个城市场景和一个开放场景）中评估了算法性能，结果显示我们的算法总体性能比现有技术提升了5%。