This paper presents an approach to the indirect simultaneous positioning task of deformable objects based on robust linear output regulation methods. Indirect control requires maneuvering control points defined on a deformable body to desired locations by manipulating points located elsewhere on the object. The proposed control scheme uses a linearization of the deformable object dynamics into a state-space model for which the inputs are the forces applied to the manipulation points and the outputs are the positions of the control points. Then, the indirect simultaneous positioning task is treated as a classical robust linear output regulation problem. The proposed controller can compensate for deformable model nonlinearities and material uncertainties. The controller performance is illustrated through simulation and experimental results obtained on a planar deformable object model. The experiment was conducted using a robot controlled to apply desired end-effector forces on the planar object.

This paper presents a model based approach to the soft tissue deformation planning. The deformable object is manipulated through boundary displacements induced by robot manipulators controlled in position. The manipulated boundaries are maneuvered such that the control points defined on the deformable object converge to the desired locations. The proposed control is based on a Jacobian transformation between the set of manipulated point displacements and the control point displacements computed using a meshless model (reproducing kernel particle method - RKPM) of the deformable object. RKPM is employed for this study as it has been proven to accurately handle large deformations and requires no re-meshing algorithms. Simulations show that a model with a coarse particle grid can produce Jacobian transforms that accurately control a more physically real and refined model. The next step is to perform a physical study on a tissue phantom interacting with a dual arm manipulator.

We report the development of a robot for radiological percutaneous interventions using uniplanar fluoroscopy, biplanar fluoroscopy, or computed tomography (CT) for needle biopsy, radio frequency ablation, cryotherapy, and other needle procedures. AcuBot is a compact six-degree-of-freedom robot for manipulating a needle or other slender surgical instrument in the confined space of the imager without inducing image artifacts. Its distinctive characteristic is its decoupled motion capability correlated to the positioning, orientation, and instrument insertion steps of the percutaneous intervention. This approach allows each step of the intervention to be performed using a separate mechanism of the robot. One major advantage of this kinematic approach is patient safety. The first feasibility experiment performed with the robot, a cadaver study of perispinal blocks under biplanar fluoroscopy, is presented. The main expected application of this system is to CT-based procedures. AcuBot has received Food and Drug Administration clearance (IDE G010331/S1), and a clinical trial of using the robot for perispinal nerve and facet blocks is presently underway at Georgetown University, Washington, DC.