Robotic grasp analysis using deformable solid mechanics

Given an object and a hand, identifying a robust grasp out of an infinite set of grasp candidates is a challenging problem, and several grasp synthesis approaches have been proposed in the robotics community to find the promising ones. Most of the approaches assume both the object and the hand to be rigid and evaluate the robustness of the grasp based on the wrenches acting at contact points. Since rigid body mechanics is used in these works, the actual distribution of the contact tractions is not considered, and contacts are represented by their resultant wrenches. However, the tractions acting at the contact interfaces play a critical role in the robustness of the grasp, and not accounting for these in detail is a serious limitation of the current approaches. In this paper, we replace the conventional wrench-based rigid-body approaches with a deformable-body mechanics formulation as is conventional in solid mechanics. We briefly review the wrench-based grasp synthesis approaches in the literature and address the drawbacks present in them from a solid mechanics standpoint. In our formulation, we account for deformation in both the grasper and the object and evaluate the robustness of grasp based on the distribution of normal and tangential tractions at the contact interface. We contrast how a given grasp situation is solved using conventional wrench space formulations and deformable solid mechanics and show how tractions on the contacting surfaces influence the grasp equilibrium. Recognizing that contact areas can be correlated to contact tractions, we propose a grasp performance index, π , based on the contact areas. We also devise a grasp analysis strategy to identify robust grasps under random perturbations and implement it using Finite Element Method (FEM) to study a few grasps. One of the key aspects of our Finite Element (FE)-based approach is that it can be used to monitor the dynamic interaction between object and hand for judging grasp robustness. We then compare our measure, π , with conventional grasp quality measures, ϵ and v and show that it successfully accounts for the effect of the physical characteristics of the object and hand (such as the mass, Young’s modulus and coefficient of friction) and identifies robust grasps that are in line with human intuition and experience.

• Robotics