All-printed soft human-machine interface for robotic physicochemical sensing

计算机科学 软机器人 3d打印 人机系统 人工智能 机器人 人机界面 接口(物质) 人机交互 工程类 生物医学工程 操作系统 气泡 最大气泡压力法
作者
You Yu,Jiahong Li,Samuel A. Solomon,Jihong Min,Jiaobing Tu,Wei Guo,Changhao Xu,Yu Song,Wei Gao
出处
期刊:Science robotics [American Association for the Advancement of Science]
卷期号:7 (67): eabn0495-eabn0495 被引量:349
标识
DOI:10.1126/scirobotics.abn0495
摘要

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.
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