Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control

TL;DR

This paper presents a decentralized NMPC approach for cooperative object transportation by multiple robots, ensuring obstacle avoidance, collision prevention, and convergence to target poses through inter-agent communication.

Contribution

It introduces a novel decentralized NMPC scheme that guarantees safe and efficient cooperative transportation with explicit feasibility and convergence analysis.

Findings

Successful simulation validation of the proposed control scheme

Effective obstacle and collision avoidance demonstrated

Ensures convergence to desired object pose

Abstract

This paper addresses the problem of cooperative transportation of an object rigidly grasped by N robotic agents. 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. The control scheme is based on inter-agent communication and is decentralized in the sense that each agent calculates its own control signal. 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.