无模型控制用于机器人技术中自主预防不良事件的控制
Meenakshi Narayan1, Ann Majewicz Fey2
1Robotics and Automation Lab, Department of Engineering Technology, Miami University, Middletown, OH, United States.
Frontiers in robotics and AI
|January 22, 2024
概括
这项研究引入了机器人辅助针干预的新型控制系统,以95%的成功率预防针曲和组织移位等不良事件. 该系统使用自适应和反应机制,以实现更安全的自主操作.
科学领域:
- 机器人技术和自主系统
- 医疗干预 医疗干预
- 控制工程 控制工程 控制工程
背景情况:
- 预防性控制对于安全的自主操作至关重要,特别是在机器人辅助的针干预中.
- 在插入过程中,可能会发生针曲和组织位移等不良事件,可能会损害器官.
- 现有的方法缺乏对这些不可预测事件的强有力的实时预防.
研究的目的:
- 开发和验证用于自主机器人辅助针干预的新型控制子程序.
- 积极预防诸如针曲和组织移位等不良事件.
- 为了提高机器人手术程序的安全性和可靠性.
主要方法:
- 由于针-组织动态的非线性,采用了无模型的控制技术.
- 开发了一种改进的无模型自适应控制 (IMFAC),以提高融合和稳定性.
- 控制子程序集成了基于预测事件的反应性 (停止) 和适应性 (转向) 机制.
主要成果:
- 与模拟中MFAC相比,IMFAC在模拟中展示了20%更快的输出收.
- 在模拟和生物组织中,集成系统成功地防止了针曲和组织位移.
- 对不良事件的实时预防在各种条件下实现了95%的成功率.
结论:
- 拟议的自适应和反应控制子程序显著提高了机器人辅助针干预的安全性.
- 在复杂的环境中,IMFAC算法为控制针插入提供了强大而高效的解决方案.
- 这一进步对于可靠和安全部署自主机器人手术系统至关重要.
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