A Curved Bimodal Hair Sensor for Airflow and Electrostatic Perception in Robotic Control

Noncontact environmental perception is crucial for autonomous alerting and behavior control in mobile robots, yet existing artificial sensory systems still fall short in integrating weak-stimulus detection, interaction-direction discrimination, and multimodal sensing within a single device. Drawing inspiration from the electroreceptive hair-like sensilla of treehoppers and the high-curvature proboscis of butterflies, we report a Curved Bimodal Hair Sensor (CBHS) that achieves dual-mode noncontact perception of electrostatic and airflow stimuli. The high-curvature hair architecture enhances mechanical coupling, and a sliding-rheostat-based length-encoded resistance mechanism enables stable detection of compressive and tensile loads down to 10⁻⁴ N. Electrostatic and airflow stimuli are inherently discriminated by their contrasting time scales and signal profiles. Under airflow, hair oscillation induces interfacial contact electrification, dynamically modulating the electrical state to enable wind-speed detection as low as 0.9 m·s⁻¹, with a random forest model classifying six speed levels at 98.89% accuracy. When integrated into a robotic platform, CBHS directly maps electrostatic polarity to approach and escape behaviors, while a four-sensor array enables airflow-direction-guided vigilance and closed-loop directional motion. This work presents a structure-driven bioinspired strategy for low-complexity multimodal artificial sensory systems directly coupled to robotic behavior.