Distributed NMPC for Cooperative Aerial Manipulation of Cable-Suspended Loads

Abstract

In this letter, we address the problem of cooperative manipulation of a cable-suspended load by a team of aerial robots. Unlike classical approaches that rely on centralized controllers, we propose a Distributed Nonlinear Model Predictive Control (DNMPC) framework in which the UAVs communicate over a peer-to-peer network a reduced amount of variables. In the proposed method, each robot handles only a small subset of the global optimization problem. The optimal motion computed by the DNMPC loop is then used as a reference for local nonlinear controllers that track the trajectory and compute the robot's actuation inputs. We validate the proposed scheme both through numerical simulations and real-world experiments on the Fly-Crane system: a rigid platform connected to three robots by pairs of cables.