This paper presents algorithms for identifying the odour source of a chemical plume with significant filament intermittency and meander developed in fluid-advected environments. The algorithms are abstracted from moth-inspired chemical plume tracing strategies in two steps. First, we introduce the concept of the last chemical detection points that leads to construction of a source identification zone and development of two variations in the source identification algorithms. Second, we use Monte Carlo methods to optimise the proposed algorithms in a simulated environment. The evaluation results demonstrate that the optimised algorithm achieves a success rate of over 90% in identifying the source location, the average identification time is 3–4 min and the average error is 1–2 m surrounding the source location.

中文翻译：

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从飞蛾启发的羽流追踪策略中提取算法，识别在流体环境中的气味源

本文介绍了用于识别化学羽流的气味源的算法，这些羽流具有显着的细丝间歇性，并且在受流体影响的环境中会产生曲折。该算法分两个步骤从受蛾启发的化学羽流追踪策略中抽象出来。首先，我们介绍最后化学检测点的概念，该化学检测点可导致源识别区的构建以及源识别算法中两种变体的发展。其次，我们使用蒙特卡洛方法在模拟环境中优化所提出的算法。评估结果表明，优化算法在识别源位置上取得了90％以上的成功率，平均识别时间为3-4 min，围绕源位置的平均误差为1-2 m。