Ultra-stable and tough bioinspired crack-based tactile sensor for small legged robots

• Published in: Npj flexible electronics Vol. 7; no. 1; pp. 22 - 12
• Main Authors: Kim, Taewi, Hong, Insic, Kim, Minho, Im, Sunghoon, Roh, Yeonwook, Kim, Changhwan, Lim, Jongcheon, Kim, Dongjin, Park, Jieun, Lee, Seunggon, Lim, Daseul, Cho, Junggwang, Huh, Seokhaeng, Jo, Seung-Un, Kim, ChangHwan, Koh, Je-Sung, Han, Seungyong, Kang, Daeshik
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20-04-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/925/927/511; Biomimetics; Chemistry and Materials Science; Collagen; Crack initiation; Crack propagation; Crack sensitivity; Durability; Electronics and Microelectronics; Flexibility; Instrumentation; Materials Science; Nanowires; Optical and Electronic Materials; Performance degradation; Polymer Sciences; Propagation; Robots; Sensors; Silver; Skin; Stress concentration; Tactile sensors (robotics); Touch
• ISSN: 2397-4621; 2397-4621
• DOI: 10.1038/s41528-023-00255-2

For legged robots, collecting tactile information is essential for stable posture and efficient gait. However, mounting sensors on small robots weighing less than 1 kg remain challenges in terms of the sensor’s durability, flexibility, sensitivity, and size. Crack-based sensors featuring ultra-sensitivity, small-size, and flexibility could be a promising candidate, but performance degradation due to crack growing by repeated use is a stumbling block. This paper presents an ultra-stable and tough bio-inspired crack-based sensor by controlling the crack depth using silver nanowire (Ag NW) mesh as a crack stop layer. The Ag NW mesh inspired by skin collagen structure effectively mitigated crack propagation. The sensor was very thin, lightweight, sensitive, and ultra-durable that maintains its sensitivity during 200,000 cycles of 0.5% strain. We demonstrate sensor’s feasibility by implementing the tactile sensation to bio-inspired robots, and propose statistical and deep learning-based analysis methods which successfully distinguished terrain type.