发表机构
Institute of Systems and Robotics; University of Coimbra（系统与机器人研究所; 科英布拉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自然环境中气味源定位难题，提出混合认知策略，将生物启发反应性融入信念依赖运动规划，经模拟和实验验证，该策略能有效减少行进距离、提高成功率并降低定位误差。
AI 中文摘要
释放到环境中的化学污染物通过湍流传输，形成复杂、间歇性的羽流结构，威胁生态系统和人类健康。快速定位排放源至关重要，配备化学传感器的现场机器人是执行此任务的可行手段。然而，由于检测稀疏和缺乏可靠的浓度梯度，从传感器读数推断源位置仍然困难。现有方法分为两种范式。生物启发策略依赖检测触发的反应行为，认知策略将观测整合到源位置的概率信念中。本文提出一种混合策略，将生物启发的反应性明确纳入依赖信念的运动规划中，引入检测触发的切换机制，行为参数直接从信念度量中导出。通过三种湍流条件下的模拟和在葡萄牙蒙德戈河的自主水面车辆现场实验验证，结果表明行进距离减少高达50%，成功率86%，平均定位误差3.2米。
英文摘要
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.
