Model Predictive Interaction Control for Robotic Manipulation Tasks

Abstract

This article presents the concept of model predictive interaction control (MPIC) as a generic, flexible, and comprehensive approach for robotic manipulation tasks. MPIC is based on the repetitive solution of an optimal control problem that includes a robot model for motion prediction as well as an interaction model for force prediction. In order to handle both elastic and rigid contact situations, a cascaded approach with low-level PD control is adopted, which allows to combine the linear-elastic environment model and the limited controller stiffness. Due to its flexibility, MPIC can be favorably used for realizing the elementary manipulation primitives (MP) within a hierarchical task planning framework, where each MP corresponds to a particular parameterization of the cost function and the constraints. The control methodology and the manipulation approach are evaluated in simulations and experiments using a 7-degree-of-freedom industrial robot.