Self-powered artificial vibrissal system with anemotaxis behavior

Anemotaxis behaviors inspired by rats have tremendous potential in efficiently processing perilous search and rescue operations in the physical world, but there is still lack of hardware components that can efficiently sense, encode, and recognize wind signal. Here, we report an artificial vibrissal system consisting of a self-powered carbon black sensor and threshold-switching HfO<sub>2</sub> memristor. By integrating a forming HfO<sub>2</sub> memristor with a self-powered angle-detecting hydro-voltaic sensor, the spiking sensory neuron can synchronously perceive and encode wind, humidity, and temperature signals into spikes with different frequencies. Furthermore, to validate the self-powered artificial vibrissal system with anemotaxis behavior, a robotic car with equipped artificial vibrissal system tracks trajectory toward the air source has been demonstrated. This design not only addresses the high energy consumption and low computing issues of traditional sensory system but also introduces the multimode functionalities, therefore promoting the construction of neuromorphic perception systems for neurorobotics.

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Publication Details

Journal
Science Advances
Published
2025-06-06
DOI
https://doi.org/10.1126/sciadv.adt3068
Citations
13
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
9.50
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article

Self-powered artificial vibrissal system with anemotaxis behavior

Su‐Ting Han, Yanyun Ren, Runze Xu, Ziyu Lv et al.
13 citations
Science Advances
Advanced Memory and Neural Computing
9.50
article

Self-powered artificial vibrissal system with anemotaxis behavior

Su‐Ting Han, Yanyun Ren, Runze Xu, Ziyu Lv, Ye Bo Zhou, Tao Sun, Meng Qi
article en
13 citations

Abstract

Anemotaxis behaviors inspired by rats have tremendous potential in efficiently processing perilous search and rescue operations in the physical world, but there is still lack of hardware components that can efficiently sense, encode, and recognize wind signal. Here, we report an artificial vibrissal system consisting of a self-powered carbon black sensor and threshold-switching HfO<sub>2</sub> memristor. By integrating a forming HfO<sub>2</sub> memristor with a self-powered angle-detecting hydro-voltaic sensor, the spiking sensory neuron can synchronously perceive and encode wind, humidity, and temperature signals into spikes with different frequencies. Furthermore, to validate the self-powered artificial vibrissal system with anemotaxis behavior, a robotic car with equipped artificial vibrissal system tracks trajectory toward the air source has been demonstrated. This design not only addresses the high energy consumption and low computing issues of traditional sensory system but also introduces the multimode functionalities, therefore promoting the construction of neuromorphic perception systems for neurorobotics.

Science AdvancesVol. 11(23)
Hong Kong Polytechnic University (HK), Shenzhen University (CN), Shanghai Institute of Microsystem and Information Technology (CN), Bohai University (CN)
Affordable and clean energy
Openalex Percentile: Top 2%
Advanced Memory and Neural Computing
9.50
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