A Survey of Distributed Optimization Methods for Multi-Robot Systems

TL;DR

This paper surveys distributed optimization methods for multi-robot systems, evaluating their practicality and performance, and highlights C-ADMM as a particularly effective approach for decentralized robotic tasks.

Contribution

It introduces a general framework for integrating distributed optimization into robotics and compares various algorithms through simulations, emphasizing C-ADMM's advantages.

Findings

C-ADMM shows strong performance in multi-robot scenarios.

Distributed optimization enables decentralized coordination without central control.

Simulation results demonstrate the practicality of different algorithms for robotic tasks.

Abstract

Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and network-constrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.