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CiiS Lab
Johns Hopkins University
112 Hackerman Hall
3400 N. Charles Street
Baltimore, MD 21218
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Lab Director
Russell Taylor
127 Hackerman Hall
rht@jhu.edu
Most robotic assisted surgical procedures are characterized by restricted access to the workspace as well as constrained manipulation of a surgical tool. In such cases, the surgeons’ ability can be augmented by techniques such as virtual fixtures. Virtual fixtures are algorithms which provide anisotropic behavior to surgeons’ motion commands in addition to filtering out tremor to provide safety and precision.
Typically, surgical tasks have a certain degree of uncertainty that arises from factors such as registration errors, variations in anatomy and changes during procedures. Consider an example task of placing a surgical tool at a point in space. Depending on the nature of the procedure, one can define a region and tool placement within this region that would lead to the expected outcome. This region could be on the order of a few microns for retinal vein cannulation or hundreds of microns for a biopsy procedure. Furthermore, we can define another region where the surgeon might deliberately want to place the instrument to account for some uncertainties inherent in surgical procedures. In other words, we would like to have some compliance in the virtual fixture, while maintaining a preferred motion. Therefore, we define 3 different regions:
The relationship of these three regions depends on the surgical task.
For this project we formalize a library of virtual fixtures for task primitives. Our paradigm covers the implementation of guidance virtual fixtures, and forbidden region virtual fixtures, with both “hard” and “soft” constraints. Our approach uses a weighted, multi-objective optimization framework to solve for incremental joint motion given the instantaneous kinematics of the manipulator and the geometric constraints. Note that we treat the robot as a purely kinematic device, independent of manipulator characteristic: teleoperative or cooperative controlled; admittance or impedance type.
We formulate the motion control of the robot as a quadratic optimization problem, in which constraints and optimization criteria are combined from multiple sources. These include:
We have applied this formulation to teleoperation of complex robots such as snake robots, assistance in tasks such as suturing, implementation of safety regions in neurosurgery, alignment aids in targeting tasks, and retinal surgery. We solve these problems at interactive rates (typically, from 20 Hz to 200 Hz, depending on problem size, using either a linearized constrained least squares solver based on the work of Lawson and Hanson or a more general solver, depending on the problem.
