计算机科学
任务(项目管理)
灵活性(工程)
弹道
机器人
概率逻辑
一般化
约束(计算机辅助设计)
机器人学
公制(单位)
人工智能
模拟
计算机视觉
数学
运营管理
统计
数学分析
物理
几何学
管理
天文
经济
作者
Gui‐Bin Bian,Zhang Chen,Zhen Li,Bing-Ting Wei,Weipeng Liu,Daniel Santos da Silva,Wanqing Wu,Victor Hugo C. de Albuquerque
标识
DOI:10.1016/j.eswa.2023.120134
摘要
Learning surgical skills from trained surgeons can increase the level of autonomy of surgical robots and provide assistance for surgeons in an appropriate way during surgery. However, the remote center of motion (RCM) constraint is a tricky problem while most other works only consider the task performed in the lesion area. This study aims to transfer the minimally invasive surgical skills demonstrated by surgeons to the surgical robot while satisfying the RCM constraint. In this paper, the implicit constraints of manipulation skills are modeled into a probabilistic model to maintain the variability and flexibility of the surgeon’s operations. A novel method is proposed to address the inconsistency between the RCM constraint space and surgical task space. The generalization of the learned skills under the RCM constraint has also been improved. We validated the proposed method in a physical experiment with a tracking task under the RCM constraint. An original measurement method based on shape similarity is proposed to compute the tracking errors of trajectories that have nonhomogeneous temporal and spatial distortions. The root means square error of the trajectory was 1.8 mm, which exceeded the average for operator demonstrations.
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