Insect-inspired adaptive behavioral compensation strategy against olfactory sensory deficiency for robotic odor source localization

Abstract The ability to navigate toward potential mates, food sources, or nesting sites is a fundamental trait shared across species. Despite their small nervous systems, insects demonstrate remarkable odor-guided navigation, making them ideal models for bioinspired robotics. Although robots capable of odor-source localization have been developed, their practical deployment remains limited, particularly under conditions involving physical impairment. In this study, we investigated how sensory deficits affect odor localization behavior in male silk moth Bombyx mori , which locates females using sex pheromones. Remarkably, even after the removal of one antenna, the primary olfactory organ, the silk moth retained its ability to locate the odor source. Behavioral analyses revealed that navigational strategy depended on the spatial position of odor detection. We replicated this strategy in a quadrupedal robot by designing a system capable of estimating odor positions analogous to moth antennae. Robotic experiments confirmed that adaptive behavioral selection based on bilateral or unilateral odor cues sustains efficient odor localization. These findings highlight fundamental principles of robust robotic navigation system development capable of maintaining performance even under sensor impairment, as required for long-duration or rescue missions.

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Publication Details

Journal
npj Robotics
Published
2026-02-09
DOI
https://doi.org/10.1038/s44182-026-00080-5
Primary Topic
Insect Pheromone Research and Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Insect-inspired adaptive behavioral compensation strategy against olfactory sensory deficiency for robotic odor source localization

Daisuke Kurabayashi, Shunsuke Shigaki, K. Yokota, Ryoko Sekiwa et al.
npj Robotics
Insect Pheromone Research and Control
article

Insect-inspired adaptive behavioral compensation strategy against olfactory sensory deficiency for robotic odor source localization

Daisuke Kurabayashi, Shunsuke Shigaki, K. Yokota, Ryoko Sekiwa, Dai Owaki
article en

Abstract

Abstract The ability to navigate toward potential mates, food sources, or nesting sites is a fundamental trait shared across species. Despite their small nervous systems, insects demonstrate remarkable odor-guided navigation, making them ideal models for bioinspired robotics. Although robots capable of odor-source localization have been developed, their practical deployment remains limited, particularly under conditions involving physical impairment. In this study, we investigated how sensory deficits affect odor localization behavior in male silk moth Bombyx mori , which locates females using sex pheromones. Remarkably, even after the removal of one antenna, the primary olfactory organ, the silk moth retained its ability to locate the odor source. Behavioral analyses revealed that navigational strategy depended on the spatial position of odor detection. We replicated this strategy in a quadrupedal robot by designing a system capable of estimating odor positions analogous to moth antennae. Robotic experiments confirmed that adaptive behavioral selection based on bilateral or unilateral odor cues sustains efficient odor localization. These findings highlight fundamental principles of robust robotic navigation system development capable of maintaining performance even under sensor impairment, as required for long-duration or rescue missions.

npj RoboticsVol. 4(1)
Meiji University (JP), National Institute of Informatics (JP), Tohoku University (JP), The University of Osaka (JP)
Japan Society for the Promotion of Science, Japan Science and Technology Agency, Precursory Research for Embryonic Science and Technology
Openalex Percentile: Top 64%
Insect Pheromone Research and Control
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Insect-inspired adaptive behavioral compensation strategy against olfactory sensory deficiency for robotic odor source localization — Daisuke Kurabayashi, Shunsuke Shigaki, et al. · npj Robotics (2026) | TGRS Research Map | TGRS