Kinematic noise propagation and grasp quality evaluation

To determine a force-closure grasp, current grasp synthesis algorithms either assume a deterministic model to compute a desired finger placement without noise, or model the end-effector position as a free-floating rigid body whose noise in pose is independent of the kinematic chain formed by the robot arm. In this work we instead explore a probabilistic approach that explicitly models noise in joint-angles. By sampling additive noise that is applied to a pre-grasp configuration and studying the resulting probability of force-closure when the robot fingers are closed, we observe in experiments that joint-angle positions can have a remarkable effect on the probability of successfully restraining an object. We systematically study the grasp quality value as a random variable and investigate the convergence of sampling based estimators for the mean, covariance and moments up to third order of this quantity by means of Montecarlo Sampling. We study illustrative examples of the impact of initial joint-configurations on the likelihood of force closure on a seven degree of freedom simulated Kuka lightweight robot arm.