基于强化学习的多自由度智能超声机器人系统  

Multi-degree-of-freedom Intelligent Ultrasound Robot System Based on Reinforcement Learning

作　　者：宁国琛 张欣然[1] 廖洪恩[1] NING Guochen;ZHANG Xinran;LIAO Hongen(Department of Biomedical Engineering,School of Medicine,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学医学院生物医学工程系,北京100084

出　　处：《电子与信息学报》2022年第1期1-10,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(82027807,81771940);北京市自然科学基金(7212202);中国博士后科学基金(2021M701928)。

摘　　要：超声机器人作为一种典型的医疗机器人,在辅助诊断与外科引导中可以有效提高超声成像效率并降低人工长时间操作导致的疲劳。为了提升超声机器人在复杂动态环境中的成像效率与稳定性,该文提出一种基于深度强化学习的超声机器人多自由度成像控制方法与系统。首先基于近端策略梯度优化的成像动作决策方法,实时生成超声探头空间动作和姿态运动决策,并实现动态环境中对目标成像动作的持续生成过程。进一步,研究根据超声机器人成像任务中面临复杂柔性环境的特点,在超声机器人运动自主决策的基础上提出超声机器人运动空间优化策略。最终实现在避免参数调整和复杂动态环境的情况下,对不同人体部位进行自动的机器人超声成像。As a typical medical robot,the efficiency of ultrasound imaging and the fatigue caused by manual operation for a long time in assisted diagnosis and surgical guidance can effectively be reduced by ultrasound robots.To improve the imaging efficiency and stability of ultrasound robots in complex dynamic environments,a deep reinforcement learning-based imaging control method and system are proposed.Firstly,an imaging action decision method based on proximal policy gradient optimization is proposed to generate spatial action and probe pose motion decisions of the ultrasound robot in real-time and to realize the continuous generation process of imaging action decisions for targets in dynamic environments.Further,based on the characteristics of the complex and flexible environment faced by the ultrasound robot in the imaging task,an ultrasound robot control optimization strategy is proposed on the basis of the autonomous ultrasound robot motion decision.Eventually,a fully autonomous robotic ultrasound imaging process for different human body parts is achieved while avoiding parameter adjustments and complex dynamic environments.

关 键 词：超声成像 超声机器人 柔性控制 强化学习 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TH776[自动化与计算机技术—控制科学与工程]