首页|State Key Laboratory of Reliability and Intelligence of Electrical Equipment Res earchers Yield New Data on Robotics (Magnetostrictive bi-perceptive flexible sen sor for tracking bend and position of human and robot hand)

State Key Laboratory of Reliability and Intelligence of Electrical Equipment Res earchers Yield New Data on Robotics (Magnetostrictive bi-perceptive flexible sen sor for tracking bend and position of human and robot hand)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news reporting out of the State Key Laboratory of Reliability an d Intelligence of Electrical Equipment by NewsRx editors, research stated, “The sensor that simultaneously perceives bending strain and magnetic field has the p otential to detect the finger bending state and hand position of the human and r obot.” Funders for this research include Natural Science Foundation of Hebei Province; National Natural Science Foundation of China. Our news editors obtained a quote from the research from State Key Laboratory of Reliability and Intelligence of Electrical Equipment: “Based on unique magneto- mechanical coupling effect of magnetostrictive materials, the proposed a bi-perc eptive flexible sensor, consisting of the Co-Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input-output characteristics of the simulation model. An asynchrono us multi-task deep learning method is proposed, which takes the output of the po sition task as the partial input of the bending state task to analyze the output information of the sensor quickly and accurately.”

State Key Laboratory of Reliability and Intelligence of Electrical EquipmentEmerging TechnologiesMachine LearningR obotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Sep.20)