多机器人协同嗅觉系统，精准定位泄漏气体源。
SniffySquad: Patchiness-Aware Gas Source Localization with Multi-Robot Collaboration
- 基于气体分布不均特性设计主动感知策略
- 成功率提升20%以上，路径效率提高30%以上
- 适合复杂环境下的工业气体泄漏搜救
气体源定位对快速应对气体泄漏灾害至关重要，移动机器人为此提供了有前景的解决方案。然而，现有方法多依赖反应式刺激或简化的气体羽流模型，在真实环境中因气体分布呈斑块状而表现不佳。本文提出SniffySquad，一种多机器人嗅觉系统，专门应对气体源定位中的斑块性问题。该系统引入感知斑块性的主动传感机制，提升数据采集质量与定位精度；同时设计创新的协作角色自适应策略，显著提高寻源效率。大量实验表明，该系统成功率提升20%以上，路径效率改善30%以上，优于当前最先进的气体源定位方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。