# A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance

## TL;DR

This paper presents a nonlinear model predictive control approach for cooperative robotic manipulation that ensures obstacle avoidance, singularity avoidance, and input saturation compliance, with proven feasibility and convergence.

## Contribution

It introduces a novel NMPC scheme for cooperative manipulation that guarantees collision, singularity avoidance, and input constraints in a bounded workspace.

## Key findings

- Successful simulation validation of the proposed control scheme.
- Guarantees on collision and singularity avoidance in cooperative manipulation.
- Convergence and feasibility of the NMPC method are theoretically proven.

## Abstract

This paper addresses the problem of cooperative transportation of an object rigidly grasped by $N$ robotic agents. In particular, we propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.