基于物理依赖引导的序列推理的深度概率室内气体源定位
Deep Probabilistic Indoor Gas Source Localization via Physical Dependency-Guided Sequential Inference
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中文总结 AI 辅助
针对室内气体源定位难题，本文提出深度概率框架，结合物理依赖的序列推理，在模拟与真实机器人实验中均优于基线方法，可实现高效准确的在线气体源定位。
中文摘要 AI 辅助
可靠的气体源定位（GSL）对工业和城市环境的安全至关重要，但在室内环境中仍具挑战性，因为墙壁和障碍物会与气流相互作用，形成复杂的气体扩散。计算流体动力学、细丝模型等高保真模型可捕捉这些效应，但其计算成本限制了在线应用。本文提出一种深度概率框架，从移动机器人采集的稀疏且含噪声的测量值中推断源的后验分布。与直接从测量值推断源估计值的端到端模型不同，该方法纳入了室内气体传输的物理依赖关系，其中风速和源位置决定浓度场。这些依赖关系通过序列条件推理嵌入，其中推断出的风场和浓度场指导源后验估计，该结构可改善稀疏且含噪声观测下的定位效果。评估表明，该方法优于代表性GSL基线，能在模拟中实现准确高效的主动GSL；真实机器人实验证明其可在嵌入式GPU上实现在线运行。
英文摘要
Reliable gas source localization (GSL) is critical to safety in industrial and urban environments, yet remains challenging indoors because walls and obstacles interact with airflow to create complex gas dispersion. High-fidelity models such as computational fluid dynamics and filament models can capture these effects, but their computational cost limits online use. We propose a deep probabilistic framework that infers the source posterior from sparse and noisy measurements collected by a mobile robot. Unlike end-to-end models that directly infer source estimates from measurements, the proposed method incorporates physical dependencies of indoor gas transport, where wind and source location govern the concentration field. These dependencies are embedded through sequential conditional inference, in which inferred wind and concentration fields guide source posterior estimation. This structure improves localization under sparse and noisy observations. Evaluations show that the proposed method outperforms representative GSL baselines and enables accurate and efficient active GSL in simulations. Real-robot experiments demonstrate the feasibility of online operation on an embedded GPU.
发表机构
- Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）
- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
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