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基于"人-机-环"信息流的机器人手术系统研究与展望

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机器人手术系统凭借微创、精细、灵活、无震颤等优势,在多个外科领域不断得到普及应用.然而,现有的机器人手术系统尚未充分发挥人和机器各自的优势,在智能化交互方面的表现有待提高.因此,文中首先从系统科学的角度分析机器人手术系统中交互关系的发展,并从多方面提出当前人机交互的不足.然后,构建面向机器人手术系统的"人-机-环"信息流框架,以机器人辅助乳内动脉获取手术场景为例,梳理"人-机-环"各部分之间的复杂交互.最后,基于"人-机结合"理论,结合构建的"人-机-环"信息流框架,提出以"人机融合智能共进"为目标的新一代机器人手术系统的设计思路,为实现更安全高效的机器人微创外科手术目标提供借鉴.
Research and Prospect of Robotic Surgical System Based on Human-Machine-Environment Information Flow
Robot-assisted surgical systems continue to gain widespread application in various surgical fields,due to their minimally invasive,precise,flexible and tremor-free attributes.However,the advantages of both humans and machines are not fully exploited in the existing robot-assisted surgical systems,and the performance should be improved in the intelligent interaction aspect.Therefore,the development of interactive relationships within robot-assisted surgical systems is analyzed from a systems science perspective and the deficiencies in human-machine interaction are discussed from multiple viewpoints.Then,an"human-machine-environment"information flow framework for robot-assisted surgical systems is constructed.Taking the example of robotic-assisted internal mammary artery acquisition scenarios,the intricate interactions among the"human-machine-environment"different components are illustrated.Finally,based on the theory of"human-machine integration"and the established"human-machine-environment"information flow framework,a design approach for a new generation of robot-assisted surgical systems with the goal of"human-machine integration and intelligent co-development"is proposed.This proposal serves as a valuable reference for realizing the goal of safer and more efficient robotic minimally invasive surgery.

MetasynthesisHuman-Machine SystemInformation FlowRobotic Surgery System

崔皓鑫、王嵘、郑楠、章颂、任瞳、梁渝靖

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中国科学院大学人工智能学院 北京 100049

中国科学院自动化研究所多模态人工智能系统全国重点实验室 北京 100190

中国人民解放军总医院第六医学中心心血管病医学部成人心脏外科 北京 100853

综合集成 人机系统 信息流 机器人手术系统

国家重点研究发展计划项目

2022YFB4700805

2024

模式识别与人工智能
中国自动化学会,国家智能计算机研究开发中心,中国科学院合肥智能机械研究所

模式识别与人工智能

CSTPCD北大核心
影响因子:0.954
ISSN:1003-6059
年,卷(期):2024.37(1)
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