将嗅觉反应与认知决策结合，提升复杂环境中污染源定位效率。
Merging Reaction to Cognition: A Hybrid Cognitive Strategy for Odour Source Localisation in Natural Environments
- 用检测触发机制在探索与直奔源头间切换，自适应调整行为。
- 实验中定位误差平均3.2米，成功率86%，距离减少50%。
- 无需人工调参，适合实际水域等复杂环境的机器人导航。
化学污染物在湍流作用下形成复杂、间歇性烟羽，威胁生态与健康。快速定位排放源至关重要，配备化学传感器的移动机器人为此提供了可行方案。然而，由于检测稀疏且缺乏可靠浓度梯度，从传感器读数推断源位置仍具挑战。现有方法分为两类：仿生策略依赖检测触发的反应行为（如冲浪-搜寻），效率高但需场景调参；认知策略将观测融合为源位置的概率信念，更鲁棒但存在过度探索且依赖信念准确性。此前马尔可夫链分析表明，检测后源向运动的发生频率约为未检测时的两倍，暗示反应行为可自然融入认知框架。本文提出一种混合策略，显式将仿生反应性嵌入基于信念的运动规划中，引入检测触发的切换机制，实现跨流探索与源向运动之间的动态转换，优先考虑靠近源而非信息增益。行为参数直接由信念指标导出，实现无需人工调参的自适应反应。通过三种湍流条件的仿真及葡萄牙蒙德戈河的自主水面车辆实地实验验证，结果表明，旅行距离最多减少50%，成功率高达86%，平均定位误差为3.2米。
原文摘要 · Abstract (English)
Chemical pollutants released into the environment are transported by turbulent flows, generating complex, intermittent plume structures that threaten ecosystems and human health. Rapid localisation of emission sources is critical, and field robots equipped with chemical sensors provide a viable means to perform this task. However, inferring source location from sensor readings remains difficult due to sparse detections and the absence of reliable concentration gradients. Existing approaches fall into two paradigms. Bio-inspired strategies rely on reactive behaviours triggered by detections, such as surge-casting, offering efficiency but requiring scenario-specific tuning. Cognitive strategies integrate observations into a probabilistic belief over source location. While more robust, they suffer from excessive exploration and strong dependence on belief accuracy. The Fast-Cognitive algorithm reduced this computational burden but preserved the fundamental limitations. Previous Markov chain analysis revealed that source-directed motions occur roughly twice as often following odour detections, indicating that reactive behaviours naturally emerge within cognitive frameworks. This work proposes a hybrid strategy that explicitly incorporates bio-inspired reactivity into belief-dependent motion planning. It introduces a detection-triggered switching mechanism formalising transitions between crossflow exploration and source-directed motion, prioritising source proximity over information gain. Behavioural parameters are derived directly from belief metrics, enabling adaptive reactivity without manual tuning. The approach is validated through simulations under three turbulence conditions and field experiments with an autonomous surface vehicle in the Mondego River, Portugal. Results show up to 50% reduction in travelled distance, 86% success rate, and 3.2m average localisation error.
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