一种基于物理信息神经网络的路径规划方法,用于柔软组织的柔性针刺穿
Li Zheng1, Fan Zhang1, Yaozong Huang1
1Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai, People's Republic of China.
概括
这项研究引入了一种新的物理信息神经网络 (PINN) 和快速探索随机树星 (RRT*) 方法,用于精确的灵活针刺穿路径规划. 这种方法提高了微创手术的实时准确性和安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
背景情况:
- 灵活针刺是一个关键的最小侵入性技术.
- 路径规划的准确性直接影响这些程序的临床结果.
- 传统的有限元方法在计算上很复杂,对实时应用不够.
研究的目的:
- 开发一种先进的软组织穿孔路径规划方法.
- 在复杂性和实时适应性方面克服传统方法的局限性.
主要方法:
- 为软组织穿孔路径规划提出了一种物理信息神经网络 (PINN) 方法.
- 在PINN模型中,使用快速探索随机树星 (RRT*) 来生成可穿孔区域以优化路径.
- 用神经网络衍生软组织力学模型和渐进式学习控制策略来实时优化和避免碰撞.
主要成果:
- 组合PINN-RRT*方法证明了动态穿孔路径校正,误差小于1毫米.
- 这种精度符合精确针定位的基本临床要求.
- 渐进式学习控制策略通过数据分析有效优化了路径预测模型.
结论:
- 开发的PINN-RRT*方法显著提高了灵活针刺的安全性和效率.
- 它解决了包括建模复杂性,路径规划困难和实时适应性在内的关键挑战.
- 这种方法减少了对专家知识的依赖,提高了可访问性和一致性.
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