Hydrogel and Machine Learning for Soft Robots’ Sensing and Signal Processing: A Review
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Abstract
Abstract The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perception, control, and signal processing. Hydrogels and machine learning provide promising solutions to the problems above. This review aims to summarize this recent trend by first assessing the current hydrogel-based sensing and actuation methods applied to soft robots. We outlined the mechanisms of perception in response to various external stimuli. Next, recent achievements of machine learning for soft robots’ sensing data processing and optimization are evaluated. Here we list the strategies for implementing machine learning models from the perspective of applications. Last, we discuss the challenges and future opportunities in perception data processing and soft robots’ high level tasks.
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Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Advanced Materials and Mechanics · Dielectric materials and actuators
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References (100+)
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