首页|Studies from Shanghai Jiao Tong University Yield New Data on Robotics (A Microsc opic Vision-based Robotic System for Floating Electrode Assembly)
Studies from Shanghai Jiao Tong University Yield New Data on Robotics (A Microsc opic Vision-based Robotic System for Floating Electrode Assembly)
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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 originating from Shanghai, People's Republic of C hina, by NewsRx correspondents, research stated, "The implantation of multichann el, miniaturized, flexible neuroelectrodes for high-quality brain signal acquisi tion is of great importance for brain science research and brain-computer interf acing (BCI). However, slender and thin flexible neuroelectrodes usually require a tungsten probe as the shuttle to assist in penetrating the pia mater for impla ntation." Financial support for this research came from Shanghai Municipal Science and Tec hnology Major Project. Our news editors obtained a quote from the research from Shanghai Jiao Tong Univ ersity, "The process in which the tungsten probe passes through the engaging hol e on the tip of the electrode and is tightly bonded is called electrode assembly , which is challenging due to the small-scale and fragile microstructures. The c onventional manual assembly is error-prone and time-consuming with low yields. I t has a high risk of electrode damage, requiring extensive training, very stable hand- eye coordination, and a high level of manual dexterity of the operator. T he development of a robot-controlled microassembly system is essential for neuro science research and clinical deployment. This article presents a universal auto mated microscopic vision-guided robotic system for brain electrode assembly. A r obot system with learning-based detection combined with visual servoing is devel oped for 3-D object and pose estimation, and a robot with submicron displacement accuracy achieves the precise control of the probe. In addition, a new end-to-e nd deep learning network is designed for microfeature detection, and a palpation -based motion strategy is proposed to enable motion control with missing depth i nformation in the microenvironment."
ShanghaiPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningRobotRoboticsRobotsShanghai J iao Tong University
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