Modeling and Motion Planning for In-hand Sliding ManipulationPublic Deposited
This thesis studies the in-hand manipulation problem of repositioning finger contacts on an object by controlled sliding. In this thesis we investigate two versions of the problem. First for a multifingered hand with circle patch contacts, we present a framework for planning the motion of the hand to create an inertial load on the grasped object to achieve a desired in-grasp sliding motion. The model of the sliding dynamics is based on a soft-finger limit surface contact model at each fingertip. A motion planner is derived to automatically solve for the finger motions for a given initial and desired configuration of the object relative to the fingers. Iterative planning and execution is shown to reduce errors that occur due to modeling and trajectory tracking errors. The framework is applied to the problem of regrasping a laminar object held in a pinch grasp. We propose a limit surface model of the contact pressure distribution at each finger to predict sliding directions. Experimental validations are shown, including iterative error reduction and repeatability of the experiment. Secondly we study quasistatic in-hand sliding manipulation with spring-sliding compliant grasps. We focus on point-contact multi-fingered grasps and the goal is to achieve object regrasping by taking advantage of external contacts with the environment. Spring compliance ensures fingers remain in contact and maps contact forces to finger compressions. By controlling finger anchor motions the contact forces can be moved to the edges of friction cones and cause sliding to realize regrasping. External contacts provide forces that maintain object force balance during the motions. We model the contact and object mechanics for multi-fingered grasps in spatial cases and analyze robust conditions in terms of finger contact wrench uncertainties. Based on the modeling a general motion planning framework is proposed. We use a two-fingered system to illustrate the analysis and detail the planning algorithm to find feasible regrasp motions maximizing robustness.