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

Abstract

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.

Advances in advanced solution‐synthesis‐based structural materials for tactile sensors and their intelligent applications

“…Conductor NWs are typically randomly interspersed on an elastic polymer substrate through methods such as spin‐coating, spray‐coating, or dip‐coating, to construct an active layer of tactile sensors 95–98 . However, the non‐uniform distribution of conductor NWs and the physical adhesion between the NWs and the polymer substrate may lead to stability issues during long‐term tactile sensing 99 .…”

Highly Sensitive and Linear Vibration‐Based Flexible Modulus Sensing System for Human Modulus Monitoring and Disease Prevention

“…Considering the periodic timing signal characteristics of the modulus application system, further application functions is able to be completed using deep learning algorithms. [41][42][43][44] Modulus classification and identification of human body parts using a Transformer Network based on the modulus sensing system. The network architecture is shown in Figure 6b, mainly using the multi-head attention algorithm.…”