解耦(概率)
粒子群优化
极限学习机
计算机科学
灵活性(工程)
控制理论(社会学)
机器人
手术机器人
光纤布拉格光栅
垂直的
人工智能
数学
算法
控制工程
工程类
光纤
统计
控制(管理)
电信
人工神经网络
几何学
作者
Bin Yao,Jianxun Zhang,Yu Dai,Guangming Xia
标识
DOI:10.1109/cac51589.2020.9327656
摘要
In minimally invasive surgical robot system, the implementation of the force feedback function can increase the flexibility of the surgeon during surgery and reduce the risk of damage to the tissues and organs of the patient. In order to achieve the force detection during surgical process, this paper designs a 3- axis force sensor based on fiber Bragg grating (FBG). When decoupling the sensor, a decoupling result superior to least square (LS) is achieved by combining extreme learning machine (ELM) and particle swarm optimization (PSO). By using PSO-ELM for decoupling, the average error rates in three mutually perpendicular directions are 1.35%, 1.07%, 5.70%, respectively.
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