Contour Moments Based Manipulation of Composite Rigid-Deformable Objects with Finite Time Model Estimation and Shape/Position Control

Abstract

The robotic manipulation of composite rigiddeformable objects (i.e. those with mixed non-homogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this paper we propose a new visual servoing method that has the capability to manipulate this broad class of objects (which varies from soft to rigid) with the same adaptive strategy. To quantify the object's infinite-dimensional configuration, our new approach computes a compact feedback vector of 2D contour moments features. A sliding mode control scheme is then designed to simultaneously ensure the finite-time convergence of both the feedback shape error and the model estimation error. The stability of the proposed framework (including the boundedness of all the signals) is rigorously proved with Lyapunov theory. Detailed simulations and experiments are presented to validate the effectiveness of the proposed approach. To the best of the author's knowledge, this is the first time that contour moments along with finite-time control have been used to solve this difficult manipulation problem.

Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds

“…Further, this formulation controls the displacements of individual points which may not fully reflect the 3D shape of the object. Other learning-free works [20,[23][24][25] represent the object shape using 2D image contours; limiting the space of controllable 3D deformations. Most recently, Shetab-Bushehri et al [26] model the deformable obeject as a 3D lattice and successfully achieve full 3D control.…”

Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds

“…For precise control, one must use a large number of feature points, making control highly susceptible to noise and occlusion. Other learning-free works [19][20][21] represent the object shape using 2D image contours; this severely limits the space of controllable 3D deformations.…”