Details

Autor(en) / Beteiligte

• Zhao, Huiqi
• Zhang, Yizheng
• Han, Lei
• Qian, Weiqi
• Wang, Jiabin
• Wu, Heting
• Li, Jingchen
• Dai, Yuan
• Zhang, Zhengyou
• Bowen, Chris R.
• Yang, Ya

Titel

Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception

Ist Teil von

• Nano-micro letters, 2024-12, Vol.16 (1), p.11-11, Article 11

Ort / Verlag

Singapore: Springer Nature Singapore

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Highlights The tactile performance of ultralight multifunctional sensors can reach the level of human tactile perception. An individual sensor can provide multiple tactile sensations: pressure, temperature, materials recognition, and 3D location. Therefore, it is no longer necessary to integrate multiple sensing modules with different functions, which greatly simplifies system complexity and reduces energy loss. The tactile system with multimodal learning algorithms has universality and can accommodate object recognition tasks in various application scenarios (e.g., Mars and Kitchen). Humans can perceive our complex world through multi-sensory fusion. Under limited visual conditions, people can sense a variety of tactile signals to identify objects accurately and rapidly. However, replicating this unique capability in robots remains a significant challenge. Here, we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure, temperature, material recognition and 3D location capabilities, which is combined with multimodal supervised learning algorithms for object recognition. The sensor exhibits human-like pressure (0.04–100 kPa) and temperature (21.5–66.2 °C) detection, millisecond response times (11 ms), a pressure sensitivity of 92.22 kPa −1 and triboelectric durability of over 6000 cycles. The devised algorithm has universality and can accommodate a range of application scenarios. The tactile system can identify common foods in a kitchen scene with 94.63% accuracy and explore the topographic and geomorphic features of a Mars scene with 100% accuracy. This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing, recognition and intelligence.