A Manipulation Control Strategy for Granular Materials Based on a Gaussian Mixture Model

Abstract

In the context of metal additive manufacturing, one of the most attractive tasks to be robotized is the cleaning process of metal powder after the printing operations. This task presents a challenging scenario for most of robot manipulation approaches in the literature. In this paper we present an approach, marker-less and real time affordable, which address the cleaning problem like a shape manipulation control problem. This control strategy is designed as an optimization problem. The error function is written as a lagrangian function using an objective function based on Gaussian Mixture Model (GMM). The local optimization is performed by a gradient descent and a global optimization process is used to avoid local minima.

Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems

“…This grasp planning pipeline is designed for nonlinear deformable objects to be manipulated by three-fingered robotic hands, but its architecture could be redesigned as a group of interchangeable modules in future works. These modules could include the following: other types of objects (e.g., changing the contact interaction model for rigid objects, granular media [53]), new sensing data (i.e., combining tactile and vision information for estimating the 3D shape of the object and the real contact forces in real-time [54]), multiple robotic fingers (e.g., dexterous robotic hands [55]), or even more complex manipulation tasks (e.g., in-hand manipulation [56]).…”