In this study, we propose a robust chemical plume tracing (CPT) algorithm for various environments. We developed this algorithm based on the behavior of insects that have CPT abilities globally. However, it is difficult to accurately estimate the behavior pattern from trajectory data because the trajectory contains a high level of measurement noise. We employed flight muscle electromyograms, whose motor commands were assumed to be generated in the same ganglion as the behavior, to estimate the behavior pattern using a support vector machine. By using the estimated results, we modeled the time-varying behavioral change of insects and verified the effectiveness of the phenomenon for CPT using the constructive approach. We named this time-varying CPT algorithm as time-varying moth-inspired algorithm. From the CPT experiment results using the robot, we found that the localization success rate improved by changing the behavior in a time-variant manner.

In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem. Solving CPT represents an important technique in the field of engineering because it can be used to perform rescue operations at the time of a disaster and to identify sources of harmful substances. An appropriate intake of air when sensing odors plays an important role in performing CPT. Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors. By experimental evaluation, we verified that the quadcopter wake intake method has good directivity and can be used to realize CPT. Concretely, even at various odor source heights, the quadcopter had a three-dimensional CPT success rate of at least 70%. These results imply that, although a further development of three-dimensional CPT is necessary in order to conduct it in unknown and cluttered environments, the intake method proposed in this paper enables a pocket-sized quadcopter to perform three-dimensional CPT.

Most animals survive and thrive due to navigational behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how the adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using a VR system, the silkmoth had the highest navigational success rate when odor, vision, and wind information were correctly provided. However, the success rate of the search was reduced if the wind direction information provided was different from the direction actually detected. This indicates that it is important to acquire not only odor information but also wind direction information correctly. When the wind is received from the same direction as the odor, the silkmoth takes positive behavior; if the odor is detected but the wind direction is not in the same direction as the odor, the silkmoth behaves more carefully. This corresponds to a modulation of behavior according to the degree of complexity (turbulence) of the environment. We mathematically modeled the modulation of behavior using multisensory information and evaluated it using simulations. The mathematical model not only succeeded in reproducing the actual silkmoth search behavior but also improved the search success relative to the conventional odor-source search algorithm.

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.

Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization.

In this study, we developed a novel unfixed-type experimental system that we call a '3-DOF servosphere.' This system comprises one sphere and three omniwheels that support the sphere. The measurement method is very simple. An experimental animal is placed on top of the sphere. The position and heading angle of the animal are observed by using a high-speed camera installed above the sphere. Because the system can rotate the sphere with three degrees of freedom (DOFs) independently, the position and heading angle at the origin can be maintained without fixing the body. This system can be used to measure an animal's natural behavior while simultaneously providing it with precise stimuli. Moreover, electrodes can be inserted at specific sites to measure biosignals with locomotion. Therefore, this system can simultaneously measure the stimulus input-internal state-locomotion output of an animal. In this study, we focused on the chemical plume tracing (CPT) behavior of the Bombyx mori male silkworm moth in order to identify its CPT algorithm for mounting on a robot. In an experiment, we simultaneously measured the stimulus input, flight muscle electromyogram (EMG), and CPT behavior by using the 3-DOF servosphere to verify the system. We elucidated the relationship between the CPT behavior and flight muscle EMG.