一个以数据驱动的模型,对RIS机器人I2进行歇斯底里补偿
Mojtaba Esfandiari1, Yanlin Zhou1, Shervin Dehghani2
1Department of Mechanical Engineering and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, USA.
概括
像蛇一样的机器人可以提高视网膜微手术的精度. 一个新的数据驱动模型准确地预测机器人运动,大大提高了微妙的眼内手术的定位精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 眼科医生 眼科 眼科
- 医疗工程 医学工程
背景情况:
- 视网膜微手术需要高精度,因为细微的组织和狭窄的眼内空间.
- 目前的仪器具有有限的灵巧性,对复杂的视网膜手术构成挑战.
- 像蛇一样的机器人可以在眼内外科手术中提高灵活性和准确性.
研究的目的:
- 开发用于视网膜微手术的蛇形机器人的数据驱动动力学模型.
- 解决歇斯底里斯对有线驱动机器人系统定位准确性的重大影响.
- 提高眼内手术中机器人仪器的灵敏度和定位准确度.
主要方法:
- 使用概率高斯混合模型 (GMM) 和高斯混合回归 (GMR) 进行数据驱动动力学建模.
- 将歇斯底里补偿算法集成到GMM-GMR模型中.
- 在两度自由度 (DOF) 集成的机器人眼内蛇 (I2RIS) 上实验验验证了该模型.
主要成果:
- 带有歇斯底里补偿的拟议模型实现了0.45°的斜率和0.39°的斜率的根平均平方误差 (RMSE).
- 与没有歇斯底里补偿的模型相比,证明了显著的准确性改进:偏向60%和斜率70%.
- 成功地以高精度预测了蛇尖曲角度.
结论:
- 带有歇斯底里补偿的数据驱动的GMM-GMR模型有效地提高了用于视网膜微手术的蛇形机器人的定位精度.
- 这种方法为改善微妙眼内手术的外科结果提供了有希望的解决方案.
- 开发的模型有助于推进眼科手术中的机器人辅助.
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