Human-Machine Collaborative Surgery Using Learned Models
In the future of surgery, tele-operated robotic assistants will offer the possibility of performing certain commonly occurring tasks autonomously. Using a natural division of tasks into subtasks, we propose a novel surgical Human- Machine Collaborative (HMC) system in which portions of a surgical task are performed autonomously under complete surgeon’s control, and other portions manually. Our system automatically identifies the completion of a manual subtask, seamlessly executes the next automated task, and then returns control back to the surgeon. Our approach is based on learning from demonstration. It uses Hidden Markov Models for the recognition of task completion and temporal curve averaging for learning the executed motions. We demonstrate our approach using a da Vinci tele-surgical robot. We show on two illustrative tasks where such human-machine collaboration is intuitive that automated control improves the usage of the master manipulator workspace. Because such a system does not limit the traditional use of the robot, but merely enhances its capabilities while leaving full control to the surgeon, it provides a safe and acceptable solution for surgical performance enhancement.
Nicolas Padoy Gregory D. Hager
The Johns Hopkins University,Baltimore,MD,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
5285-5292
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）