A novel framework based on a data-driven approach for modelling the behaviour of organisms in chemical plume tracing.

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.

Identification of Exploration and Exploitation Balance in the Silkmoth Olfactory Search Behavior by Information-Theoretic Modeling.

Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the "programmed behavior" of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

Analysis of the role of wind information for efficient chemical plume tracing based on optogenetic silkworm moth behavior.

Many animals use olfactory information to search for feeding areas and other individuals in real time and with high efficiency. We focus on the chemical plume tracing (CPT) ability of male silkworm moths and investigate an efficient CPT strategy for an autonomous robot. In the case of flying insects, the wind direction is an important factor in CPT, because the wind carries odors amongst other environmental information. However, whether the same phenomenon occurs in the walking silkworm moth has not been investigated. Therefore, we examine how the silkworm moth uses wind information during CPT. To accurately investigate the response to the wind direction, we introduce an optogenetic approach that replaces the odor stimulation with light stimulation, allowing us to separate the 'wind stimulus' from the 'odor stimulus'. We examine how the moth uses wind direction information in a biological experiment, and find that the movement speed is significantly reduced when the wind speed is relatively fast (1.0 m s-1). By implementing this phenomenon in an autonomous robot, we can improve the successful search rate over that of the conventional moth-inspired algorithm. Regarding the search time, the proposed algorithm finds the odor source faster in a low-frequency odorant emission environment, whereas the search is slower than the conventional method when the odor frequency is higher. Therefore, switching from the use of wind direction information to odor information according to the frequency with which the odor is encountered leads to efficient CPT performance.