Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots' movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated environments but fall short in real-world gas environments characterized by their patchy distribution. In this work, we introduce SniffySquad, a multi-robot olfaction-based system designed to address the inherent patchiness in gas source localization. SniffySquad incorporates a patchiness-aware active sensing approach that enhances the quality of data collection and estimation. Moreover, it features an innovative collaborative role adaptation strategy to boost the efficiency of source-seeking endeavors. Extensive evaluations demonstrate that our system achieves an increase in the success rate by $20\%+$ and an improvement in path efficiency by $30\%+$, outperforming state-of-the-art gas source localization solutions.

翻译：气体源定位对于快速缓解气体泄漏灾害至关重要，而移动机器人正成为一种前景广阔的解决方案。然而，现有方法主要基于反应性刺激或简化的气体羽流模型来调度机器人运动。这些方法通常在理想化的模拟环境中表现出色，但在以斑块状分布为特征的真实世界气体环境中则效果欠佳。在本工作中，我们提出了SniffySquad，一个基于多机器人嗅觉的系统，旨在解决气体源定位中固有的斑块性问题。SniffySquad采用了一种斑块感知的主动感知方法，以提高数据收集和估计的质量。此外，它还具备一种创新的协作角色自适应策略，以提升源搜索工作的效率。大量评估表明，我们的系统将成功率提高了$20\%+$，路径效率提升了$30\%+$，性能优于最先进的气体源定位解决方案。