首页|University of Utah Researcher Updates Current Data on Robotics (Magnetically-Act uated Endoluminal Soft Robot With Electroactive Polymer Actuation for Enhanced G ait Performance)

University of Utah Researcher Updates Current Data on Robotics (Magnetically-Act uated Endoluminal Soft Robot With Electroactive Polymer Actuation for Enhanced G ait Performance)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on robotics is the subjec t of a new report. According to news originating from the University of Utah by NewsRx correspondents, research stated, “Endoluminal devices are indispensable i n medical procedures in the natural lumina of the body, such as the circulatory system and gastrointestinal tract.” Financial supporters for this research include Division of Emerging Frontiers in Research And Innovation; Office of International Science And Engineering. Our news reporters obtained a quote from the research from University of Utah: “ In current clinical practice, there is a need for increased control and capabili ties of endoluminal devices with less discomfort and risk to the patient. This p aper describes the detailed modeling and experimental validation of a magneto-el ectroactive endoluminal soft (MEESo) robot concept that combines magnetic and el ectroactive polymer (EAP) actuation to improve the utility of the device. The pr oposed capsule-like device comprises two permanent magnets with alternating pola rity connected by a soft, low-power ionic polymer-metal composite (IPMC) EAP bod y. A detailed model of the MEESo robot is developed to explore quantitatively th e effects of dual magneto-electroactive actuation on the robot’s performance. It is shown that the robot’s gait is enhanced, during the magnetically-driven gait cycle, with IPMC body deformation. The concept is further validated by creating a physical prototype MEESo robot.”

University of UtahEmerging Technologie sMachine LearningRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Sep.17)