Finding the source of an odor dispersed by a turbulent flow is a vital task for many organisms. When many individuals concurrently perform the same olfactory search task, sharing information about other members' decisions can potentially boost the performance. But how much of this information is actually exploitable for the collective task? Here we show, in a model of a swarm of agents inspired by moth behavior, that there is an optimal way to blend the private information about odor and wind detections with the public information about other agents' heading direction. Our results suggest an efficient multiagent olfactory search algorithm that could prove useful in robotics, e.g., in the identification of sources of harmful volatile compounds.

中文翻译：

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在动荡的环境中进行集体嗅觉搜索。

对于许多生物来说，寻找由湍流散发的气味的来源是一项至关重要的任务。当许多人同时执行相同的嗅觉搜索任务时，共享有关其他成员决策的信息可能会提高性能。但是，实际上有多少信息可用于集体任务？在这里，我们以受蛾类行为启发的一群特工的模型表明，存在一种将气味和风的检测的私人信息与其他特工的前进方向的公共信息混合的最佳方法。我们的结果提出了一种有效的多主体嗅觉搜索算法，该算法可证明对机器人技术有用，例如，在识别有害挥发性化合物的来源中。