We propose a data-driven approach for modelling an organism's behaviour instead of conventional model-based strategies in chemical plume tracing (CPT). CPT models based on this approach show promise in faithfully reproducing organisms’ CPT behaviour. To construct the data-driven CPT model, a training dataset of the odour stimuli input toward the organism is needed, along with an output of the organism’s CPT behaviour. To this end, we constructed a measurement system comprising an array of alcohol sensors for the measurement of the input and a camera for tracking the output in a real scenario. Then, we determined a transfer function describing the input–output relationship as a stochastic process by applying Gaussian process regression, and established the data-driven CPT model based on measurements of the organism’s CPT behaviour. Through CPT experiments in simulations and a real environment, we evaluated the performance of the data-driven CPT model and compared its success rate with those obtained from conventional model-based strategies. As a result, the proposed data-driven CPT model demonstrated a better success rate than those obtained from conventional model-based strategies. Moreover, we considered that the data-driven CPT model could reflect the aspect of an organism’s adaptability that modulated its behaviour with respect to the surrounding environment. However, these useful results came from the CPT experiments conducted in simple settings of simulations and a real environment. If making the condition of the CPT experiments more complex, we confirmed that the data-driven CPT model would be less effective for locating an odour source. In this way, this paper not only poses major contributions toward the development of a novel framework based on a data-driven approach for modelling an organism’s CPT behaviour, but also displays a research limitation of a data-driven approach at this stage.

我们提出了一种数据驱动的方法来模拟生物体的行为，而不是化学羽流追踪 (CPT) 中传统的基于模型的策略。基于这种方法的 CPT 模型有望忠实再现生物体的 CPT 行为。为了构建数据驱动的 CPT 模型，需要输入生物体的气味刺激训练数据集，以及生物体 CPT 行为的输出。为此，我们构建了一个测量系统，包括用于测量输入的酒精传感器阵列和用于在真实场景中跟踪输出的相机。然后，我们通过应用高斯过程回归确定了一个将输入-输出关系描述为随机过程的传递函数，并基于对生物体 CPT 行为的测量建立了数据驱动的 CPT 模型。通过模拟和真实环境中的 CPT 实验，我们评估了数据驱动的 CPT 模型的性能，并将其成功率与传统的基于模型的策略获得的成功率进行了比较。因此，所提出的数据驱动的 CPT 模型比传统的基于模型的策略获得的成功率更高。此外，我们认为数据驱动的 CPT 模型可以反映生物体的适应性方面，该适应性调节其相对于周围环境的行为。然而，这些有用的结果来自在简单的模拟设置和真实环境中进行的 CPT 实验。如果使 CPT 实验的条件更加复杂，我们证实数据驱动的 CPT 模型对于定位气味源的效果会较差。这样，