Efficient grasping point selection for robotic-assisted tissue manipulation based on inverse finite element method

Abstract Accurate 3D deformation control of deformable soft tissues is of paramount importance in robotic-assisted surgeries. Selecting optimal grasping points is a fundamental challenge, as the deformation behavior is highly dependent on the applied forces and their locations. This paper presents an efficient grasping point selection algorithm using optimization-based inverse finite element method for tissue manipulation tasks. We propose a method for the automatic identification of optimal grasping points that minimize feature or shape errors during deformation tasks. Specifically, we formulate the grasping task as a quadratic programming problem while considering the complex mechanical coupling within the tissue structure. Our method effectively accommodates both discrete key points and point clouds as input, and can simultaneously determine multiple optimal grasping points in one optimization process. We validate the proposed method in simulation on a tissue and liver model, demonstrating its feasibility and efficiency in various scenarios. Real-world experiments are conducted on a silicone liver phantom to further validate the effectiveness of our proposed method.