Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics
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arXiv
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| Format: | Preprint |
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2024
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| author | Wang, Shengbo Fang, Mingchao Song, Lekai Li, Cong Zhang, Jian Nathan, Arokia Hu, Guohua Gao, Shuo |
| author_facet | Wang, Shengbo Fang, Mingchao Song, Lekai Li, Cong Zhang, Jian Nathan, Arokia Hu, Guohua Gao, Shuo |
| contents | Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including 'threshold,' 'no-adaptation,' and 'sensitization.' The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics Wang, Shengbo Fang, Mingchao Song, Lekai Li, Cong Zhang, Jian Nathan, Arokia Hu, Guohua Gao, Shuo Applied Physics Signal Processing Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including 'threshold,' 'no-adaptation,' and 'sensitization.' The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments. |
| title | Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics |
| topic | Applied Physics Signal Processing |
| url | https://arxiv.org/abs/2406.09304 |