Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of “tumbling” and “swimming” behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the “brain,” body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.

单细胞大肠杆菌中的细菌趋化性是最简单的生物，即使使用单个传感器，它也能够执行有效的搜索行为，通过梯度信息引导的一系列“翻滚”和“游泳”行为实现。最近的研究表明，合适的随机游走策略可以在没有梯度信息的情况下指导行为。本文介绍了一种受细菌趋化性和具体智能概念启发的新颖且简约的生物启发搜索策略：该概念指出智能行为是“大脑”之间相互作用的结果，身体形态包括由形态调整的感觉灵敏度，以及环境。具体来说，我们提出了基于生物波动框架的具有和不具有梯度信息的细菌趋化性启发的搜索行为：一个解释生物如何在其行为中利用噪音的数学框架。通过对搜索移动目标的单传感器移动机器人的广泛模拟，我们将展示搜索的有效性如何取决于感觉灵敏度和机器人大脑产生的固有随机游走策略，包括 Ballistic、Levy、Brownian和平稳搜索。结果证明了具身智能的重要性，即使是在受最简单生物启发的行为中也是如此。我们将展示搜索的有效性如何取决于感官灵敏度和机器人大脑产生的固有随机游走策略，包括弹道、列维、布朗和静止搜索。结果证明了具身智能的重要性，即使是在受最简单生物启发的行为中也是如此。我们将展示搜索的有效性如何取决于感官灵敏度和机器人大脑产生的固有随机游走策略，包括弹道、列维、布朗和静止搜索。结果证明了具身智能的重要性，即使是在受最简单生物启发的行为中也是如此。