Terrestrial animals such as ants, mice and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies are poorly understood. Tracking behavior features zig-zagging paths with animals often staying in close contact with the trail. Upon sustained loss of contact, animals execute a characteristic sequence of sweeping "casts" -- wide oscillations with increasing amplitude. Here, we provide a unified description of trail-tracking behavior by introducing an optimization framework where animals search in the angular sector defined by their estimate of the trail's heading and its uncertainty. In silico experiments using reinforcement learning based on this hypothesis recapitulate experimentally observed tracking patterns. We show that search geometry imposes limits on the tracking speed, and quantify its dependence on trail statistics and memory of past contacts. By formulating trail-tracking as a Bellman-type sequential optimization problem, we quantify the basic geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and bio-mimetic robots, and formulate trail-tracking as a novel behavioral paradigm for learning, memory and planning.

中文翻译：

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气味追踪的行业搜索策略

诸如蚂蚁，小鼠和狗之类的陆生动物经常使用表面附着的气味踪迹来建立航行路线或寻找食物和伴侣，但对其追踪策略知之甚少。跟踪行为具有锯齿形路径，动物经常与路径保持紧密接触。一旦失去持续的接触，动物便会执行一系列特征性的“横扫”动作，即振幅不断增大的宽幅振荡。在这里，我们通过引入优化框架来提供对跟踪行为的统一描述，在优化框架中，动物将在其对航向及其不确定性的估计所定义的角扇区中进行搜索。在基于该假设的强化学习的计算机模拟实验中，概括了实验观察到的跟踪模式。我们表明搜索几何对跟踪速度施加了限制，并量化了其对跟踪统计信息和过去联系人记忆的依赖性。通过将踪迹跟踪定义为Bellman型顺序优化问题，我们量化了最佳扇形搜索策略的基本几何元素，从而有效地解释了为什么以及何时需要铸造。我们提出了一组实验来推断追踪动物如何获取，整合和响应追踪路径上的过去信息。更笼统地说，我们定义了与动物和仿生机器人相关的导航策略，并将跟踪作为一种新的行为范式进行学习，记忆和规划。我们量化了最佳扇形搜索策略的基本几何元素，有效地解释了为什么以及何时需要铸造。我们提出了一组实验来推断追踪动物如何获取，整合和响应追踪路径上的过去信息。更笼统地说，我们定义了与动物和仿生机器人相关的导航策略，并将跟踪作为一种新的行为范式进行学习，记忆和规划。我们量化了最佳扇形搜索策略的基本几何元素，有效地解释了为什么以及何时需要铸造。我们提出了一组实验来推断追踪动物如何获取，整合和响应追踪路径上的过去信息。更笼统地说，我们定义了与动物和仿生机器人相关的导航策略，并将跟踪作为一种新的行为范式进行学习，记忆和规划。