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 durabili...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.Crackbased 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.展开更多
基金the Defense Acquisition Program Administration’s Critical Technology R&D program(No.UC190002D).
文摘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.Crackbased 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.
