Silkmoth-inspired Adaptive Sensing Control for Odor Source Localization of Walking Robots

This study presents a silkmoth-inspired adaptive sensing control (SA) for enhancing odor source localization in walking robots. Inspired by the adaptive airflow regulation observed in silkmoths, the SA adaptively adjusts fan speed in an odor-intaking system, resulting in smoother and more consistent gas concentration signals. Experimental results demonstrate that this adaptive strategy significantly improves odor detection reliability, reduces path deviation, and increases the navigation success rate compared to non-adaptive method (NA). The approach requires minimal computational resources and is effectively combined with simple gradient-based navigation, making it suitable for practical robotic applications in real-world odor localization scenarios.