Once they can create these maps, the collaborators intend to use them to begin semi-automating various surgical sub-tasks, such as tying off a suture, resecting a tumor or ablating tissue. For example, the resection sub-task would allow a surgeon to instruct his robot to resect tissue from point "a" to "b" to "c" to "d" to a depth of five millimeters and the robot would then cut out the tissue specified.
The researchers also intend to create what they call "virtual fixtures." These are pre-programmed restrictions on the robot's actions. For example, a robot might be instructed not to cut in an area where a major blood vessel has been identified. Not only would this prevent the robot from cutting a blood vessel when operating autonomously, but it would also prevent a surgeon from doing so accidentally when operating the robot manually.
"We will design the robot to be aware of what it is touching and then use this information to assist the surgeon in carrying out surgical tasks safely," Simaan said.
The Johns Hopkins team led by Taylor will develop the system infrastructure for the CSA framework, with special emphasis on the interfaces used by the surgeon. The software will be based on Johns Hopkins' open-source"Surgical Assistant Workstation" toolkit, permitting researchers both within and outside the team to access the results of the research and adapt them for other projects.
The teams will be using several different experimental robots during this research, but all the systems will share a common surgeon interface based on mechanical components from early model da Vinci surgical robots donated by Intuitive Surgical (Sunnyvale, California) and interfaced to control electronics designed by Jo
|Contact: David Salisbury|