A hybrid position/force control approach for identification of deformation models of skin and underlying tissues.

In this paper, the focus is on the design of two biomechanical models representing the skin as well as the underlying tissues behavior and properties during a robotized harvesting process. The first model is quasi-static (i.e., without considering velocity) in the pressure direction of the tool: it is principally issued from the work of d'Aulignac et al. and some interesting properties are exhibited from it. The second model is new and takes into account velocity and lubrication in the motion direction of the tool. The goal of this study is to improve skin harvesting process in robotized reconstructive surgery, by automatically selecting the force applied on the donor area and tuning the gain factors of the control law prior to harvesting. It requires extracting relevant parameters such as skin thickness and stiffness, friction coefficient, etc. that characterize the biomechanical properties of the skin and underlying tissues of each patient and of different harvesting surfaces on a given patient (thigh, skull, buttocks, ...). Due to the surgical constraint, the in vivo procedure should be performed in the operating room before starting the operation with the robot itself thanks to a suitable hybrid position/force controller. In this paper, a survey about soft tissue modeling is presented. Mathematical models are discussed along with identification protocols, and two models are chosen that meet our requirements. Finally, experimental results are presented on foam and human skin.