Animals exhibit remarkable adaptability in sensing their environments, employing strategies that optimize information gathering. For instance, silk moths adjust their wingflapping frequency to detect pheromones, while dogs modify their sniffing behavior by altering sniff height and frequency based on proximity to an odor source. Despite the potential to enhance odor detection for olfactory navigation by drawing inspiration from these natural mechanisms, many existing approaches focus on computationally intensive methods like multi-sensory integration or rely on multiple robots for odor localization, rather than leveraging embodied sensing. In this study, we propose an embodied adaptive sensing strategy that enhances odor detection by implementing an active odor sensor on a legged robot and applying a bio-inspired adaptive robot height control system for dynamically adapting the robot's height based on real-time gas concentration feedback. The control system employs a simple artificial hormone mechanism to regulate the robot height by processing gas concentration derivatives, mimicking biological adaptability. By utilizing the interaction between the active odor sensor, adaptive control system, and the legged body, this approach allows the robot to optimize its height online to capture the maximum gas concentration, thereby reducing the need for complex algorithms and high computational resources. As a result, it offers a more efficient solution for odor-driven tasks, with potential applications in real-world environments.

In this study, we propose an insect-inspired odor intake method for carrying out chemical plume tracing (CPT) in an outdoor environment and experimentally investigated the relationship between the intake state and behavioral states of search. Tracking chemical plumes is important because it facilitates the identification of an odor source. Previous studies have focused on the development of CPT algorithms to improve CPT performance. However, there is still inadequate discussion on the effect of the amount of odor acquisition, which is an important factor in determining the state of searching behavior, on CPT performance. To address this issue, we first designed and developed an insect-inspired odor-intake device. To evaluate the performance of the device, we conducted CPT experiments in an outdoor environment. An insect-inspired CPT algorithm that relies only on odor information was adopted because the purpose of this study was to evaluate the performance of the odor intake device. As a result of the outdoor CPT experiment, it was found that by setting the appropriate combination of the odor intake state and the behavioral state, certain search performances can be maintained even in highly unpredictable environments, such as outdoors. This suggests that in order to improve the CPT performance, it is important not only to improve the algorithm but also to devise an odor intake method and to combine it with the behavioral state appropriately.

We used cells, which are the units that make up a living body, as building blocks to design a biomachine hybrid system and develop a tactile sensor that uses living cells as sensor receptors. We fabricated a novel cell tactile sensor with the electrodes formed using printed electronics technology. This sensor comprises elastic electrodes mounted on a soft material to acquire tactile information; similar to a conventional cell tactile sensor, it acquires signals through mechanical stimulation. Further, self-organization of cells can be induced, and logical processing such as selective responses to stimuli can be performed directly by the physical system, without any coding using programming languages. The proposed novel cell tactile sensor that uses printed electrodes is small enough to mount on robots. Interestingly, we confirmed the self-healing properties of the proposed sensor after cells were injured mechanically.

In this paper, we focus on the chemical plume tracing (CPT) problem, which is a known engineering challenge. In nature, animals solve the CPT by adaptively modifying their behavior according to the environment. Therefore, we propose a CPT solution method with high engineering value by modeling the CPT algorithm of an animal. In this paper, we consider a male silkworm moth as a model. To perform CPT in a turbulent environment, the adaptive selection of the behavior plays an important role. Therefore, we performed simultaneous measurement experiments involving CPT behavior of the brain and analyzed the links between the brain's neural activity and behavioral patterns. We measured the brain's neural response in the lateral accessory lobe (LAL), which generates motion commands. We employed fuzzy inference to analyze the relationship between CPT behavior and LAL neural activity. As a result of analyzing the relationship between CPT behavior and LAL, we found that the moth modulates the behavior of the state transition probability depending on the odor frequency. We modeled the obtained phenomenon and verified its effectiveness through a constructive method. As a result, the search performance was improved compared to the conventional moth algorithm.