Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

TL;DR

This paper demonstrates the practical implementation of decentralized distributed model predictive control for robot formations, showing real-time operation and effectiveness through experimental results.

Contribution

It introduces a decentralized optimization approach combining ADMM and SQP for formation control, with proven convergence and no need for a central coordinator.

Findings

Successful real-time formation control of mobile robots

Decentralized algorithms achieve convergence without central coordination

Experimental validation confirms practical feasibility

Abstract

Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.